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is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Tao%2C+X&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Tao%2C+X&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Tao%2C+X&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.22064">arXiv:2503.22064</a> <span> [<a href="https://arxiv.org/pdf/2503.22064">pdf</a>, <a href="https://arxiv.org/format/2503.22064">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Multi-Task Semantic Communications via Large Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ni%2C+W">Wanli Ni</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+H">Haofeng Sun</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.22064v1-abstract-short" style="display: inline;"> Artificial intelligence (AI) promises to revolutionize the design, optimization and management of next-generation communication systems. In this article, we explore the integration of large AI models (LAMs) into semantic communications (SemCom) by leveraging their multi-modal data processing and generation capabilities. Although LAMs bring unprecedented abilities to extract semantics from raw data… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.22064v1-abstract-full').style.display = 'inline'; document.getElementById('2503.22064v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.22064v1-abstract-full" style="display: none;"> Artificial intelligence (AI) promises to revolutionize the design, optimization and management of next-generation communication systems. In this article, we explore the integration of large AI models (LAMs) into semantic communications (SemCom) by leveraging their multi-modal data processing and generation capabilities. Although LAMs bring unprecedented abilities to extract semantics from raw data, this integration entails multifaceted challenges including high resource demands, model complexity, and the need for adaptability across diverse modalities and tasks. To overcome these challenges, we propose a LAM-based multi-task SemCom (MTSC) architecture, which includes an adaptive model compression strategy and a federated split fine-tuning approach to facilitate the efficient deployment of LAM-based semantic models in resource-limited networks. Furthermore, a retrieval-augmented generation scheme is implemented to synthesize the most recent local and global knowledge bases to enhance the accuracy of semantic extraction and content generation, thereby improving the inference performance. Finally, simulation results demonstrate the efficacy of the proposed LAM-based MTSC architecture, highlighting the performance enhancements across various downstream tasks under varying channel conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.22064v1-abstract-full').style.display = 'none'; document.getElementById('2503.22064v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.08089">arXiv:2503.08089</a> <span> [<a href="https://arxiv.org/pdf/2503.08089">pdf</a>, <a href="https://arxiv.org/format/2503.08089">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Vehicle Platooning Safety via Control Node Placement and Sizing under State and Input Bounds </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=She%2C+Y">Yifei She</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shen Wang</a>, <a href="/search/eess?searchtype=author&query=Taha%2C+A">Ahmad Taha</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.08089v1-abstract-short" style="display: inline;"> Vehicle platooning with Cooperative Adaptive Cruise Control improves traffic efficiency, reduces energy consumption, and enhances safety but remains vulnerable to cyber-attacks that disrupt communication and cause unsafe actions. To address these risks, this paper investigates control node placement and input bound optimization to balance safety and defense efficiency under various conditions. We… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.08089v1-abstract-full').style.display = 'inline'; document.getElementById('2503.08089v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.08089v1-abstract-full" style="display: none;"> Vehicle platooning with Cooperative Adaptive Cruise Control improves traffic efficiency, reduces energy consumption, and enhances safety but remains vulnerable to cyber-attacks that disrupt communication and cause unsafe actions. To address these risks, this paper investigates control node placement and input bound optimization to balance safety and defense efficiency under various conditions. We propose a two-stage actuator placement and actuator saturation approach, which focuses on identifying key actuators that maximize the system's controllability while operating under state and input constraints. By strategically placing and limiting the input bounds of critical actuators, we ensure that vehicles maintain safe distances even under attack. Simulation results show that our method effectively mitigates the impact of attacks while preserving defense efficiency, offering a robust solution to vehicle platooning safety challenges. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.08089v1-abstract-full').style.display = 'none'; document.getElementById('2503.08089v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.07911">arXiv:2503.07911</a> <span> [<a href="https://arxiv.org/pdf/2503.07911">pdf</a>, <a href="https://arxiv.org/format/2503.07911">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Visual and Text Prompt Segmentation: A Novel Multi-Model Framework for Remote Sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zi%2C+X">Xing Zi</a>, <a href="/search/eess?searchtype=author&query=Jin%2C+K">Kairui Jin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xian Tao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+J">Jun Li</a>, <a href="/search/eess?searchtype=author&query=Braytee%2C+A">Ali Braytee</a>, <a href="/search/eess?searchtype=author&query=Shah%2C+R+R">Rajiv Ratn Shah</a>, <a href="/search/eess?searchtype=author&query=Prasad%2C+M">Mukesh Prasad</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.07911v1-abstract-short" style="display: inline;"> Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks. Despite their advances, challenges specific to remote sensing remain substantial. Firstly, The SAM without clear prompt constraints, often generates redundant masks, and making post-processing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.07911v1-abstract-full').style.display = 'inline'; document.getElementById('2503.07911v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.07911v1-abstract-full" style="display: none;"> Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks. Despite their advances, challenges specific to remote sensing remain substantial. Firstly, The SAM without clear prompt constraints, often generates redundant masks, and making post-processing more complex. Secondly, the CLIP model, mainly designed for global feature alignment in foundational models, often overlooks local objects crucial to remote sensing. This oversight leads to inaccurate recognition or misplaced focus in multi-target remote sensing imagery. Thirdly, both models have not been pre-trained on multi-scale aerial views, increasing the likelihood of detection failures. To tackle these challenges, we introduce the innovative VTPSeg pipeline, utilizing the strengths of Grounding DINO, CLIP, and SAM for enhanced open-vocabulary image segmentation. The Grounding DINO+(GD+) module generates initial candidate bounding boxes, while the CLIP Filter++(CLIP++) module uses a combination of visual and textual prompts to refine and filter out irrelevant object bounding boxes, ensuring that only pertinent objects are considered. Subsequently, these refined bounding boxes serve as specific prompts for the FastSAM model, which executes precise segmentation. Our VTPSeg is validated by experimental and ablation study results on five popular remote sensing image segmentation datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.07911v1-abstract-full').style.display = 'none'; document.getElementById('2503.07911v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Under Review - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.17212">arXiv:2502.17212</a> <span> [<a href="https://arxiv.org/pdf/2502.17212">pdf</a>, <a href="https://arxiv.org/format/2502.17212">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> A Two-step Linear Mixing Model for Unmixing under Hyperspectral Variability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Haijen%2C+X">Xander Haijen</a>, <a href="/search/eess?searchtype=author&query=Koirala%2C+B">Bikram Koirala</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xuanwen Tao</a>, <a href="/search/eess?searchtype=author&query=Scheunders%2C+P">Paul Scheunders</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.17212v2-abstract-short" style="display: inline;"> Spectral unmixing is an important task in the research field of hyperspectral image processing. It can be thought of as a regression problem, where the observed variable (i.e., an image pixel) is to be found as a function of the response variables (i.e., the pure materials in a scene, called endmembers). The Linear Mixing Model (LMM) has received a great deal of attention, due to its simplicity an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17212v2-abstract-full').style.display = 'inline'; document.getElementById('2502.17212v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.17212v2-abstract-full" style="display: none;"> Spectral unmixing is an important task in the research field of hyperspectral image processing. It can be thought of as a regression problem, where the observed variable (i.e., an image pixel) is to be found as a function of the response variables (i.e., the pure materials in a scene, called endmembers). The Linear Mixing Model (LMM) has received a great deal of attention, due to its simplicity and ease of use in, e.g., optimization problems. Its biggest flaw is that it assumes that any pure material can be characterized by one unique spectrum throughout the entire scene. In many cases this is incorrect: the endmembers face a significant amount of spectral variability caused by, e.g., illumination conditions, atmospheric effects, or intrinsic variability. Researchers have suggested several generalizations of the LMM to mitigate this effect. However, most models lead to ill-posed and highly non-convex optimization problems, which are hard to solve and have hyperparameters that are difficult to tune. In this paper, we propose a two-step LMM that bridges the gap between model complexity and computational tractability. We show that this model leads to only a mildly non-convex optimization problem, which we solve with an interior-point solver. This method requires virtually no hyperparameter tuning, and can therefore be used easily and quickly in a wide range of unmixing tasks. We show that the model is competitive and in some cases superior to existing and well-established unmixing methods and algorithms. We do this through several experiments on synthetic data, real-life satellite data, and hybrid synthetic-real data. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17212v2-abstract-full').style.display = 'none'; document.getElementById('2502.17212v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 10 figures, 5 tables. This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.16970">arXiv:2502.16970</a> <span> [<a href="https://arxiv.org/pdf/2502.16970">pdf</a>, <a href="https://arxiv.org/format/2502.16970">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> 220 GHz RIS-Aided Multi-user Terahertz Communication System: Prototype Design and Over-the-Air Experimental Trials </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+Y">Yanzhao Hou</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+G">Guoning Wang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+C">Chen Chen</a>, <a href="/search/eess?searchtype=author&query=Mu%2C+G">Gaoze Mu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+Q">Qimei Cui</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Y">Yuanmu Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.16970v1-abstract-short" style="display: inline;"> Terahertz (THz) communication technology is regarded as a promising enabler for achieving ultra-high data rate transmission in next-generation communication systems. To mitigate the high path loss in THz systems, the transmitting beams are typically narrow and highly directional, which makes it difficult for a single beam to serve multiple users simultaneously. To address this challenge, reconfigu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.16970v1-abstract-full').style.display = 'inline'; document.getElementById('2502.16970v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.16970v1-abstract-full" style="display: none;"> Terahertz (THz) communication technology is regarded as a promising enabler for achieving ultra-high data rate transmission in next-generation communication systems. To mitigate the high path loss in THz systems, the transmitting beams are typically narrow and highly directional, which makes it difficult for a single beam to serve multiple users simultaneously. To address this challenge, reconfigurable intelligent surfaces (RIS), which can dynamically manipulate the wireless propagation environment, have been integrated into THz communication systems to extend coverage. Existing works mostly remain theoretical analysis and simulation, while prototype validation of RIS-assisted THz communication systems is scarce. In this paper, we designed a liquid crystal-based RIS operating at 220 GHz supporting both single-user and multi-user communication scenarios, followed by a RIS-aided THz communication system prototype. To enhance the system performance, we developed a beamforming method including a real-time power feedback control, which is compatible with both single-beam and multibeam modes. To support simultaneous multi-user transmission, we designed an OFDM-based resource allocation scheme. In our experiments, the received power gain with RIS is no less than 10 dB in the single-beam mode, and no less than 5 dB in the multi-beam mode. With the assistance of RIS, the achievable rate of the system could reach 2.341 Gbps with 3 users sharing 400 MHz bandwidth and the bit error rate (BER) of the system decreased sharply. Finally, an image transmission experiment was conducted to vividly show that the receiver could recover the transmitted information correctly with the help of RIS. The experimental results also demonstrated that the received signal quality was enhanced through power feedback adjustments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.16970v1-abstract-full').style.display = 'none'; document.getElementById('2502.16970v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.15258">arXiv:2502.15258</a> <span> [<a href="https://arxiv.org/pdf/2502.15258">pdf</a>, <a href="https://arxiv.org/format/2502.15258">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> On Performance of LoRa Fluid Antenna Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Mu%2C+G">Gaoze Mu</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+Y">Yanzhao Hou</a>, <a href="/search/eess?searchtype=author&query=Wong%2C+K">Kai-Kit Wong</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+M">Mingjie Chen</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+Q">Qimei Cui</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Ping Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.15258v1-abstract-short" style="display: inline;"> This paper advocates a fluid antenna system (FAS) assisting long-range communication (LoRa-FAS) for Internet-of-Things (IoT) applications. Our focus is on pilot sequence overhead and placement for FAS. Specifically, we consider embedding pilot sequences within symbols to reduce the equivalent symbol error rate (SER), leveraging the fact that the pilot sequences do not convey source information and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.15258v1-abstract-full').style.display = 'inline'; document.getElementById('2502.15258v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.15258v1-abstract-full" style="display: none;"> This paper advocates a fluid antenna system (FAS) assisting long-range communication (LoRa-FAS) for Internet-of-Things (IoT) applications. Our focus is on pilot sequence overhead and placement for FAS. Specifically, we consider embedding pilot sequences within symbols to reduce the equivalent symbol error rate (SER), leveraging the fact that the pilot sequences do not convey source information and correlation detection at the LoRa receiver needs not be performed across the entire symbol. We obtain closed-form approximations for the probability density function (PDF) and cumulative distribution function (CDF) of the FAS channel, assuming perfect channel state information (CSI). Moreover, the approximate SER, hence the bit error rate (BER), of the proposed LoRa-FAS is derived. Simulation results indicate that substantial SER gains can be achieved by FAS within the LoRa framework, even with a limited size of FAS. Furthermore, our analytical results align well with that of the Clarke's exact spatial correlation model. Finally, the correlation factor for the block correlation model should be selected as the proportion of the exact correlation matrix's eigenvalues greater than $1$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.15258v1-abstract-full').style.display = 'none'; document.getElementById('2502.15258v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 7 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15588">arXiv:2501.15588</a> <span> [<a href="https://arxiv.org/pdf/2501.15588">pdf</a>, <a href="https://arxiv.org/format/2501.15588">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Luo%2C+G">Gongning Luo</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+M">Mingwang Xu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+H">Hongyu Chen</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+X">Xinjie Liang</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xing Tao</a>, <a href="/search/eess?searchtype=author&query=Ni%2C+D">Dong Ni</a>, <a href="/search/eess?searchtype=author&query=Jeong%2C+H">Hyunsu Jeong</a>, <a href="/search/eess?searchtype=author&query=Kim%2C+C">Chulhong Kim</a>, <a href="/search/eess?searchtype=author&query=Stock%2C+R">Raphael Stock</a>, <a href="/search/eess?searchtype=author&query=Baumgartner%2C+M">Michael Baumgartner</a>, <a href="/search/eess?searchtype=author&query=Kirchhoff%2C+Y">Yannick Kirchhoff</a>, <a href="/search/eess?searchtype=author&query=Rokuss%2C+M">Maximilian Rokuss</a>, <a href="/search/eess?searchtype=author&query=Maier-Hein%2C+K">Klaus Maier-Hein</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Z">Zhikai Yang</a>, <a href="/search/eess?searchtype=author&query=Fan%2C+T">Tianyu Fan</a>, <a href="/search/eess?searchtype=author&query=Boutry%2C+N">Nicolas Boutry</a>, <a href="/search/eess?searchtype=author&query=Tereshchenko%2C+D">Dmitry Tereshchenko</a>, <a href="/search/eess?searchtype=author&query=Moine%2C+A">Arthur Moine</a>, <a href="/search/eess?searchtype=author&query=Charmetant%2C+M">Maximilien Charmetant</a>, <a href="/search/eess?searchtype=author&query=Sauer%2C+J">Jan Sauer</a>, <a href="/search/eess?searchtype=author&query=Du%2C+H">Hao Du</a>, <a href="/search/eess?searchtype=author&query=Bai%2C+X">Xiang-Hui Bai</a>, <a href="/search/eess?searchtype=author&query=Raikar%2C+V+P">Vipul Pai Raikar</a>, <a href="/search/eess?searchtype=author&query=Montoya-del-Angel%2C+R">Ricardo Montoya-del-Angel</a>, <a href="/search/eess?searchtype=author&query=Marti%2C+R">Robert Marti</a> , et al. (12 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.15588v1-abstract-short" style="display: inline;"> Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key componen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15588v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15588v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15588v1-abstract-full" style="display: none;"> Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://tdsc-abus2023.grand-challenge.org/ to benchmark and inspire future developments in algorithmic research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15588v1-abstract-full').style.display = 'none'; document.getElementById('2501.15588v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.10182">arXiv:2501.10182</a> <span> [<a href="https://arxiv.org/pdf/2501.10182">pdf</a>, <a href="https://arxiv.org/format/2501.10182">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Secure Semantic Communication With Homomorphic Encryption </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Meng%2C+R">Rui Meng</a>, <a href="/search/eess?searchtype=author&query=Fan%2C+D">Dayu Fan</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+H">Haixiao Gao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+Y">Yifan Yuan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+B">Bizhu Wang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+X">Xiaodong Xu</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+M">Mengying Sun</a>, <a href="/search/eess?searchtype=author&query=Dong%2C+C">Chen Dong</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Ping Zhang</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.10182v1-abstract-short" style="display: inline;"> In recent years, Semantic Communication (SemCom), which aims to achieve efficient and reliable transmission of meaning between agents, has garnered significant attention from both academia and industry. To ensure the security of communication systems, encryption techniques are employed to safeguard confidentiality and integrity. However, traditional cryptography-based encryption algorithms encount… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.10182v1-abstract-full').style.display = 'inline'; document.getElementById('2501.10182v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.10182v1-abstract-full" style="display: none;"> In recent years, Semantic Communication (SemCom), which aims to achieve efficient and reliable transmission of meaning between agents, has garnered significant attention from both academia and industry. To ensure the security of communication systems, encryption techniques are employed to safeguard confidentiality and integrity. However, traditional cryptography-based encryption algorithms encounter obstacles when applied to SemCom. Motivated by this, this paper explores the feasibility of applying homomorphic encryption to SemCom. Initially, we review the encryption algorithms utilized in mobile communication systems and analyze the challenges associated with their application to SemCom. Subsequently, we employ scale-invariant feature transform to demonstrate that semantic features can be preserved in homomorphic encrypted ciphertext. Based on this finding, we propose a task-oriented SemCom scheme secured through homomorphic encryption. We design the privacy preserved deep joint source-channel coding (JSCC) encoder and decoder, and the frequency of key updates can be adjusted according to service requirements without compromising transmission performance. Simulation results validate that, when compared to plaintext images, the proposed scheme can achieve almost the same classification accuracy performance when dealing with homomorphic ciphertext images. Furthermore, we provide potential future research directions for homomorphic encrypted SemCom. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.10182v1-abstract-full').style.display = 'none'; document.getElementById('2501.10182v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.00842">arXiv:2501.00842</a> <span> [<a href="https://arxiv.org/pdf/2501.00842">pdf</a>, <a href="https://arxiv.org/format/2501.00842">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Survey of Secure Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Meng%2C+R">Rui Meng</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+S">Song Gao</a>, <a href="/search/eess?searchtype=author&query=Fan%2C+D">Dayu Fan</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+H">Haixiao Gao</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yining Wang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+X">Xiaodong Xu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+B">Bizhu Wang</a>, <a href="/search/eess?searchtype=author&query=Lv%2C+S">Suyu Lv</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhidi Zhang</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+M">Mengying Sun</a>, <a href="/search/eess?searchtype=author&query=Han%2C+S">Shujun Han</a>, <a href="/search/eess?searchtype=author&query=Dong%2C+C">Chen Dong</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Ping Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.00842v2-abstract-short" style="display: inline;"> Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of ``Shannon's trap" by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network managem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.00842v2-abstract-full').style.display = 'inline'; document.getElementById('2501.00842v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.00842v2-abstract-full" style="display: none;"> Semantic communication (SemCom) is regarded as a promising and revolutionary technology in 6G, aiming to transcend the constraints of ``Shannon's trap" by filtering out redundant information and extracting the core of effective data. Compared to traditional communication paradigms, SemCom offers several notable advantages, such as reducing the burden on data transmission, enhancing network management efficiency, and optimizing resource allocation. Numerous researchers have extensively explored SemCom from various perspectives, including network architecture, theoretical analysis, potential technologies, and future applications. However, as SemCom continues to evolve, a multitude of security and privacy concerns have arisen, posing threats to the confidentiality, integrity, and availability of SemCom systems. This paper presents a comprehensive survey of the technologies that can be utilized to secure SemCom. Firstly, we elaborate on the entire life cycle of SemCom, which includes the model training, model transfer, and semantic information transmission phases. Then, we identify the security and privacy issues that emerge during these three stages. Furthermore, we summarize the techniques available to mitigate these security and privacy threats, including data cleaning, robust learning, defensive strategies against backdoor attacks, adversarial training, differential privacy, cryptography, blockchain technology, model compression, and physical-layer security. Lastly, this paper outlines future research directions to guide researchers in related fields. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.00842v2-abstract-full').style.display = 'none'; document.getElementById('2501.00842v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">160 pages, 27 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.14538">arXiv:2412.14538</a> <span> [<a href="https://arxiv.org/pdf/2412.14538">pdf</a>, <a href="https://arxiv.org/format/2412.14538">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cui%2C+Q">Qimei Cui</a>, <a href="/search/eess?searchtype=author&query=You%2C+X">Xiaohu You</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+N">Ni Wei</a>, <a href="/search/eess?searchtype=author&query=Nan%2C+G">Guoshun Nan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xuefei Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jianhua Zhang</a>, <a href="/search/eess?searchtype=author&query=Lyu%2C+X">Xinchen Lyu</a>, <a href="/search/eess?searchtype=author&query=Ai%2C+M">Ming Ai</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Ping Zhang</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Q">Qingqing Wu</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+M">Meixia Tao</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Y">Yongming Huang</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+C">Chongwen Huang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+G">Guangyi Liu</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+C">Chenghui Peng</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+Z">Zhiwen Pan</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+T">Tao Sun</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+T">Tao Chen</a>, <a href="/search/eess?searchtype=author&query=Khan%2C+M+K">Muhammad Khurram Khan</a>, <a href="/search/eess?searchtype=author&query=Jamalipour%2C+A">Abbas Jamalipour</a>, <a href="/search/eess?searchtype=author&query=Guizani%2C+M">Mohsen Guizani</a>, <a href="/search/eess?searchtype=author&query=Yuen%2C+C">Chau Yuen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.14538v4-abstract-short" style="display: inline;"> With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance, par… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.14538v4-abstract-full').style.display = 'inline'; document.getElementById('2412.14538v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.14538v4-abstract-full" style="display: none;"> With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance, particularly in intricate and dynamic environments. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on emphasizing their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, delineated across three progressive developmental stages. The first stage, AI for Network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, Network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies, such as digital twins for AI and semantic communication. In the final stage, AI as a Service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. In addition, we conduct an in-depth analysis of the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.14538v4-abstract-full').style.display = 'none'; document.getElementById('2412.14538v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.08628">arXiv:2411.08628</a> <span> [<a href="https://arxiv.org/pdf/2411.08628">pdf</a>, <a href="https://arxiv.org/format/2411.08628">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> TDGCN-Based Mobile Multiuser Physical-Layer Authentication for EI-Enabled IIoT </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Meng%2C+R">Rui Meng</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+H">Hangyu Zhao</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+B">Bingxuan Xu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yining Wang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+X">Xiaodong Xu</a>, <a href="/search/eess?searchtype=author&query=Lv%2C+S">Suyu Lv</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Ping Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.08628v1-abstract-short" style="display: inline;"> Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Industrial Internet of Things (IIoT). However, state-of-the-art Channel State Information (CSI)-based PLA schemes face challenges in recognizing mobile multi-users due to the limited re… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08628v1-abstract-full').style.display = 'inline'; document.getElementById('2411.08628v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.08628v1-abstract-full" style="display: none;"> Physical-Layer Authentication (PLA) offers endogenous security, lightweight implementation, and high reliability, making it a promising complement to upper-layer security methods in Edge Intelligence (EI)-empowered Industrial Internet of Things (IIoT). However, state-of-the-art Channel State Information (CSI)-based PLA schemes face challenges in recognizing mobile multi-users due to the limited reliability of CSI fingerprints in low Signal-to-Noise Ratio (SNR) environments and the constantly shifting CSI distributions with user movements. To address these issues, we propose a Temporal Dynamic Graph Convolutional Network (TDGCN)-based PLA scheme. This scheme harnesses Intelligent Reflecting Surfaces (IRSs) to refine CSI fingerprint precision and employs Graph Neural Networks (GNNs) to capture the spatio-temporal dynamics induced by user movements and IRS deployments. Specifically, we partition hierarchical CSI fingerprints into multivariate time series and utilize dynamic GNNs to capture their associations. Additionally, Temporal Convolutional Networks (TCNs) handle temporal dependencies within each CSI fingerprint dimension. Dynamic Graph Isomorphism Networks (GINs) and cascade node clustering pooling further enable efficient information aggregation and reduced computational complexity. Simulations demonstrate the proposed scheme's superior authentication accuracy compared to seven baseline schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.08628v1-abstract-full').style.display = 'none'; document.getElementById('2411.08628v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 12 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.14200">arXiv:2410.14200</a> <span> [<a href="https://arxiv.org/pdf/2410.14200">pdf</a>, <a href="https://arxiv.org/format/2410.14200">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lai%2C+H">Haoran Lai</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+Z">Zihang Jiang</a>, <a href="/search/eess?searchtype=author&query=Yao%2C+Q">Qingsong Yao</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+R">Rongsheng Wang</a>, <a href="/search/eess?searchtype=author&query=He%2C+Z">Zhiyang He</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaodong Tao</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+W">Wei Wei</a>, <a href="/search/eess?searchtype=author&query=Lv%2C+W">Weifu Lv</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+S+K">S. Kevin Zhou</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.14200v1-abstract-short" style="display: inline;"> The development of 3D medical vision-language models holds significant potential for disease diagnosis and patient treatment. However, compared to 2D medical images, 3D medical images, such as CT scans, face challenges related to limited training data and high dimension, which severely restrict the progress of 3D medical vision-language models. To address these issues, we collect a large amount of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14200v1-abstract-full').style.display = 'inline'; document.getElementById('2410.14200v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.14200v1-abstract-full" style="display: none;"> The development of 3D medical vision-language models holds significant potential for disease diagnosis and patient treatment. However, compared to 2D medical images, 3D medical images, such as CT scans, face challenges related to limited training data and high dimension, which severely restrict the progress of 3D medical vision-language models. To address these issues, we collect a large amount of unlabeled 3D CT data and utilize self-supervised learning to construct a 3D visual foundation model for extracting 3D visual features. Then, we apply 3D spatial convolutions to aggregate and project high-level image features, reducing computational complexity while preserving spatial information. We also construct two instruction-tuning datasets based on BIMCV-R and CT-RATE to fine-tune the 3D vision-language model. Our model demonstrates superior performance compared to existing methods in report generation, visual question answering, and disease diagnosis. Code and data will be made publicly available soon. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14200v1-abstract-full').style.display = 'none'; document.getElementById('2410.14200v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.06004">arXiv:2410.06004</a> <span> [<a href="https://arxiv.org/pdf/2410.06004">pdf</a>, <a href="https://arxiv.org/format/2410.06004">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Corrections to "Computer Vision Aided mmWave Beam Alignment in V2X Communications" </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xu%2C+W">Weihua Xu</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Feifei Gao</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jianhua Zhang</a>, <a href="/search/eess?searchtype=author&query=Alkhateeb%2C+A">Ahmed Alkhateeb</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.06004v3-abstract-short" style="display: inline;"> In this document, we revise the results of [1] based on more reasonable assumptions regarding data shuffling and parameter setup of deep neural networks (DNNs). Thus, the simulation results can now more reasonably demonstrate the performance of both the proposed and compared beam alignment methods. We revise the simulation steps and make moderate modifications to the design of the vehicle distribu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.06004v3-abstract-full').style.display = 'inline'; document.getElementById('2410.06004v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.06004v3-abstract-full" style="display: none;"> In this document, we revise the results of [1] based on more reasonable assumptions regarding data shuffling and parameter setup of deep neural networks (DNNs). Thus, the simulation results can now more reasonably demonstrate the performance of both the proposed and compared beam alignment methods. We revise the simulation steps and make moderate modifications to the design of the vehicle distribution feature (VDF) for the proposed vision based beam alignment when the MS location is available (VBALA). Specifically, we replace the 2D grids of the VDF with 3D grids and utilize the vehicle locations to expand the dimensions of the VDF. Then, we revise the simulation results of Fig. 11, Fig. 12, Fig. 13, Fig. 14, and Fig. 15 in [1] to reaffirm the validity of the conclusions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.06004v3-abstract-full').style.display = 'none'; document.getElementById('2410.06004v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">3 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.11289">arXiv:2408.11289</a> <span> [<a href="https://arxiv.org/pdf/2408.11289">pdf</a>, <a href="https://arxiv.org/format/2408.11289">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Mingya Zhang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhihao Chen</a>, <a href="/search/eess?searchtype=author&query=Ge%2C+Y">Yiyuan Ge</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xianping Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.11289v2-abstract-short" style="display: inline;"> In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computational complexity poses a challenge for Transformers. State Space Models (SSMs), su… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11289v2-abstract-full').style.display = 'inline'; document.getElementById('2408.11289v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11289v2-abstract-full" style="display: none;"> In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computational complexity poses a challenge for Transformers. State Space Models (SSMs), such as Mamba, have been recognized as a promising method. They not only demonstrate superior performance in modeling long-range interactions, but also preserve a linear computational complexity. The hybrid mechanism of SSM (State Space Model) and Transformer, after meticulous design, can enhance its capability for efficient modeling of visual features. Extensive experiments have demonstrated that integrating the self-attention mechanism into the hybrid part behind the layers of Mamba's architecture can greatly improve the modeling capacity to capture long-range spatial dependencies. In this paper, leveraging the hybrid mechanism of SSM, we propose a U-shape architecture model for medical image segmentation, named Hybird Transformer vision Mamba UNet (HTM-UNet). We conduct comprehensive experiments on the ISIC17, ISIC18, CVC-300, CVC-ClinicDB, Kvasir, CVC-ColonDB, ETIS-Larib PolypDB public datasets and ZD-LCI-GIM private dataset. The results indicate that HTM-UNet exhibits competitive performance in medical image segmentation tasks. Our code is available at https://github.com/simzhangbest/HMT-Unet. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11289v2-abstract-full').style.display = 'none'; document.getElementById('2408.11289v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:2403.09157; text overlap with arXiv:2407.08083 by other authors</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.09381">arXiv:2408.09381</a> <span> [<a href="https://arxiv.org/pdf/2408.09381">pdf</a>, <a href="https://arxiv.org/format/2408.09381">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Channel Estimation, Interpolation and Extrapolation in Doubly-dispersive Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gong%2C+Z">Zijun Gong</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+F">Fan Jiang</a>, <a href="/search/eess?searchtype=author&query=Song%2C+Y">Yuhui Song</a>, <a href="/search/eess?searchtype=author&query=Li%2C+C">Cheng Li</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.09381v1-abstract-short" style="display: inline;"> The OTFS (Orthogonal Time Frequency Space) is widely acknowledged for its ability to combat Doppler spread in time-varying channels. In this paper, another advantage of OTFS over OFDM (Orthogonal Frequency Division Multiplexing) will be demonstrated: much reduced channel training overhead. Specifically, the sparsity of the channel in delay-Doppler (D-D) domain implies strong correlation of channel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09381v1-abstract-full').style.display = 'inline'; document.getElementById('2408.09381v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.09381v1-abstract-full" style="display: none;"> The OTFS (Orthogonal Time Frequency Space) is widely acknowledged for its ability to combat Doppler spread in time-varying channels. In this paper, another advantage of OTFS over OFDM (Orthogonal Frequency Division Multiplexing) will be demonstrated: much reduced channel training overhead. Specifically, the sparsity of the channel in delay-Doppler (D-D) domain implies strong correlation of channel gains in time-frequency (T-F) domain, which can be harnessed to reduce channel training overhead through interpolation. An immediate question is how much training overhead is needed in doubly-dispersive channels? A conventional belief is that the overhead is only dependent on the product of delay and Doppler spreads, but we will show that it's also dependent on the T-F window size. The finite T-F window leads to infinite spreading in D-D domain, and aliasing will be inevitable after sampling in T-F domain. Two direct consequences of the aliasing are increased channel training overhead and interference. Another factor contributing to channel estimation error is the inter-symbol-carrier-interference (ISCI), resulting from the uncertainty principle. Both aliasing and ISCI are considered in channel modelling, a low-complexity algorithm is proposed for channel estimation and interpolation through FFT. A large T-F window is necessary for reduced channel training overhead and aliasing, but increases processing delay. Fortunately, we show that the proposed algorithm can be implemented in a pipeline fashion. Further more, we showed that data-aided channel tracking is possible in D-D domain to further reduce the channel estimation frequency, i.e., channel extrapolation. The impacts of aliasing and ISCI on channel interpolation error are analyzed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09381v1-abstract-full').style.display = 'none'; document.getElementById('2408.09381v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.04535">arXiv:2408.04535</a> <span> [<a href="https://arxiv.org/pdf/2408.04535">pdf</a>, <a href="https://arxiv.org/format/2408.04535">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Synchronous Multi-modal Semantic Communication System with Packet-level Coding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tian%2C+Y">Yun Tian</a>, <a href="/search/eess?searchtype=author&query=Ying%2C+J">Jingkai Ying</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Jin%2C+Y">Ye Jin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.04535v2-abstract-short" style="display: inline;"> Although the semantic communication with joint semantic-channel coding design has shown promising performance in transmitting data of different modalities over physical layer channels, the synchronization and packet-level forward error correction of multimodal semantics have not been well studied. Due to the independent design of semantic encoders, synchronizing multimodal features in both the sem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04535v2-abstract-full').style.display = 'inline'; document.getElementById('2408.04535v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.04535v2-abstract-full" style="display: none;"> Although the semantic communication with joint semantic-channel coding design has shown promising performance in transmitting data of different modalities over physical layer channels, the synchronization and packet-level forward error correction of multimodal semantics have not been well studied. Due to the independent design of semantic encoders, synchronizing multimodal features in both the semantic and time domains is a challenging problem. In this paper, we take the facial video and speech transmission as an example and propose a Synchronous Multimodal Semantic Communication System (SyncSC) with Packet-Level Coding. To achieve semantic and time synchronization, 3D Morphable Mode (3DMM) coefficients and text are transmitted as semantics, and we propose a semantic codec that achieves similar quality of reconstruction and synchronization with lower bandwidth, compared to traditional methods. To protect semantic packets under the erasure channel, we propose a packet-Level Forward Error Correction (FEC) method, called PacSC, that maintains a certain visual quality performance even at high packet loss rates. Particularly, for text packets, a text packet loss concealment module, called TextPC, based on Bidirectional Encoder Representations from Transformers (BERT) is proposed, which significantly improves the performance of traditional FEC methods. The simulation results show that our proposed SyncSC reduce transmission overhead and achieve high-quality synchronous transmission of video and speech over the packet loss network. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04535v2-abstract-full').style.display = 'none'; document.getElementById('2408.04535v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.03651">arXiv:2408.03651</a> <span> [<a href="https://arxiv.org/pdf/2408.03651">pdf</a>, <a href="https://arxiv.org/format/2408.03651">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Path-SAM2: Transfer SAM2 for digital pathology semantic segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Mingya Zhang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+L">Liang Wang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhihao Chen</a>, <a href="/search/eess?searchtype=author&query=Ge%2C+Y">Yiyuan Ge</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xianping Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.03651v2-abstract-short" style="display: inline;"> The semantic segmentation task in pathology plays an indispensable role in assisting physicians in determining the condition of tissue lesions. With the proposal of Segment Anything Model (SAM), more and more foundation models have seen rapid development in the field of image segmentation. Recently, SAM2 has garnered widespread attention in both natural image and medical image segmentation. Compar… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03651v2-abstract-full').style.display = 'inline'; document.getElementById('2408.03651v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.03651v2-abstract-full" style="display: none;"> The semantic segmentation task in pathology plays an indispensable role in assisting physicians in determining the condition of tissue lesions. With the proposal of Segment Anything Model (SAM), more and more foundation models have seen rapid development in the field of image segmentation. Recently, SAM2 has garnered widespread attention in both natural image and medical image segmentation. Compared to SAM, it has significantly improved in terms of segmentation accuracy and generalization performance. We compared the foundational models based on SAM and found that their performance in semantic segmentation of pathological images was hardly satisfactory. In this paper, we propose Path-SAM2, which for the first time adapts the SAM2 model to cater to the task of pathological semantic segmentation. We integrate the largest pretrained vision encoder for histopathology (UNI) with the original SAM2 encoder, adding more pathology-based prior knowledge. Additionally, we introduce a learnable Kolmogorov-Arnold Networks (KAN) classification module to replace the manual prompt process. In three adenoma pathological datasets, Path-SAM2 has achieved state-of-the-art performance.This study demonstrates the great potential of adapting SAM2 to pathology image segmentation tasks. We plan to release the code and model weights for this paper at: https://github.com/simzhangbest/SAM2PATH <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03651v2-abstract-full').style.display = 'none'; document.getElementById('2408.03651v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages , 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.10469">arXiv:2406.10469</a> <span> [<a href="https://arxiv.org/pdf/2406.10469">pdf</a>, <a href="https://arxiv.org/format/2406.10469">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Object-Attribute-Relation Representation Based Video Semantic Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Du%2C+Q">Qiyuan Du</a>, <a href="/search/eess?searchtype=author&query=Duan%2C+Y">Yiping Duan</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Q">Qianqian Yang</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Debbah%2C+M">M茅rouane Debbah</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.10469v2-abstract-short" style="display: inline;"> With the rapid growth of multimedia data volume, there is an increasing need for efficient video transmission in applications such as virtual reality and future video streaming services. Semantic communication is emerging as a vital technique for ensuring efficient and reliable transmission in low-bandwidth, high-noise settings. However, most current approaches focus on joint source-channel coding… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.10469v2-abstract-full').style.display = 'inline'; document.getElementById('2406.10469v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.10469v2-abstract-full" style="display: none;"> With the rapid growth of multimedia data volume, there is an increasing need for efficient video transmission in applications such as virtual reality and future video streaming services. Semantic communication is emerging as a vital technique for ensuring efficient and reliable transmission in low-bandwidth, high-noise settings. However, most current approaches focus on joint source-channel coding (JSCC) that depends on end-to-end training. These methods often lack an interpretable semantic representation and struggle with adaptability to various downstream tasks. In this paper, we introduce the use of object-attribute-relation (OAR) as a semantic framework for videos to facilitate low bit-rate coding and enhance the JSCC process for more effective video transmission. We utilize OAR sequences for both low bit-rate representation and generative video reconstruction. Additionally, we incorporate OAR into the image JSCC model to prioritize communication resources for areas more critical to downstream tasks. Our experiments on traffic surveillance video datasets assess the effectiveness of our approach in terms of video transmission performance. The empirical findings demonstrate that our OAR-based video coding method not only outperforms H.265 coding at lower bit-rates but also synergizes with JSCC to deliver robust and efficient video transmission. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.10469v2-abstract-full').style.display = 'none'; document.getElementById('2406.10469v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.10514">arXiv:2405.10514</a> <span> [<a href="https://arxiv.org/pdf/2405.10514">pdf</a>, <a href="https://arxiv.org/format/2405.10514">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TWC.2024.3511612">10.1109/TWC.2024.3511612 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Secrecy Performance Analysis of Multi-Functional RIS-Assisted NOMA Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pei%2C+Y">Yingjie Pei</a>, <a href="/search/eess?searchtype=author&query=Ni%2C+W">Wanli Ni</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jin Xu</a>, <a href="/search/eess?searchtype=author&query=Yue%2C+X">Xinwei Yue</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.10514v2-abstract-short" style="display: inline;"> Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-ort… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10514v2-abstract-full').style.display = 'inline'; document.getElementById('2405.10514v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.10514v2-abstract-full" style="display: none;"> Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive the secrecy outage probability (SOP) and secrecy throughput expressions of users in MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS; ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity; and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10514v2-abstract-full').style.display = 'none'; document.getElementById('2405.10514v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 9 figures, accept by IEEE transactions on wireless communication for publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.08096">arXiv:2405.08096</a> <span> [<a href="https://arxiv.org/pdf/2405.08096">pdf</a>, <a href="https://arxiv.org/ps/2405.08096">ps</a>, <a href="https://arxiv.org/format/2405.08096">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Semantic MIMO Systems for Speech-to-Text Transmission </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Weng%2C+Z">Zhenzi Weng</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Xie%2C+H">Huiqiang Xie</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.08096v2-abstract-short" style="display: inline;"> Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08096v2-abstract-full').style.display = 'inline'; document.getElementById('2405.08096v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.08096v2-abstract-full" style="display: none;"> Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve the speech-to-text task at the receiver is first designed, which compresses the semantic information and generates the low-dimensional semantic features by leveraging the transformer module. In addition, a novel semantic-aware network is proposed to facilitate transmission with high semantic fidelity by identifying the critical semantic information and guaranteeing its accurate recovery. Furthermore, we extend the SAC-ST with a neural network-enabled channel estimation network to mitigate the dependence on accurate channel state information and validate the feasibility of SAC-ST in practical communication environments. Simulation results will show that the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over the MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime. Moreover, the SAC-ST with the developed channel estimation network is comparable to the SAC-ST with perfect channel state information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08096v2-abstract-full').style.display = 'none'; document.getElementById('2405.08096v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.16913">arXiv:2404.16913</a> <span> [<a href="https://arxiv.org/pdf/2404.16913">pdf</a>, <a href="https://arxiv.org/format/2404.16913">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> DE-CGAN: Boosting rTMS Treatment Prediction with Diversity Enhancing Conditional Generative Adversarial Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Squires%2C+M">Matthew Squires</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaohui Tao</a>, <a href="/search/eess?searchtype=author&query=Elangovan%2C+S">Soman Elangovan</a>, <a href="/search/eess?searchtype=author&query=Gururajan%2C+R">Raj Gururajan</a>, <a href="/search/eess?searchtype=author&query=Xie%2C+H">Haoran Xie</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+X">Xujuan Zhou</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yuefeng Li</a>, <a href="/search/eess?searchtype=author&query=Acharya%2C+U+R">U Rajendra Acharya</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.16913v1-abstract-short" style="display: inline;"> Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent. Emerging evidence suggests that artificial intelligence can predict rTMS treatment outcomes for most patients using fMRI connectivity features. While these models can reliably predict treatment outcomes for many patients… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.16913v1-abstract-full').style.display = 'inline'; document.getElementById('2404.16913v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.16913v1-abstract-full" style="display: none;"> Repetitive Transcranial Magnetic Stimulation (rTMS) is a well-supported, evidence-based treatment for depression. However, patterns of response to this treatment are inconsistent. Emerging evidence suggests that artificial intelligence can predict rTMS treatment outcomes for most patients using fMRI connectivity features. While these models can reliably predict treatment outcomes for many patients for some underrepresented fMRI connectivity measures DNN models are unable to reliably predict treatment outcomes. As such we propose a novel method, Diversity Enhancing Conditional General Adversarial Network (DE-CGAN) for oversampling these underrepresented examples. DE-CGAN creates synthetic examples in difficult-to-classify regions by first identifying these data points and then creating conditioned synthetic examples to enhance data diversity. Through empirical experiments we show that a classification model trained using a diversity enhanced training set outperforms traditional data augmentation techniques and existing benchmark results. This work shows that increasing the diversity of a training dataset can improve classification model performance. Furthermore, this work provides evidence for the utility of synthetic patients providing larger more robust datasets for both AI researchers and psychiatrists to explore variable relationships. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.16913v1-abstract-full').style.display = 'none'; document.getElementById('2404.16913v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.09222">arXiv:2403.09222</a> <span> [<a href="https://arxiv.org/pdf/2403.09222">pdf</a>, <a href="https://arxiv.org/format/2403.09222">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Robust Semantic Communication System for Image </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Peng%2C+X">Xiang Peng</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+J">Jianhua Lu</a>, <a href="/search/eess?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.09222v1-abstract-short" style="display: inline;"> Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in image, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09222v1-abstract-full').style.display = 'inline'; document.getElementById('2403.09222v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.09222v1-abstract-full" style="display: none;"> Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in image, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09222v1-abstract-full').style.display = 'none'; document.getElementById('2403.09222v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.09157">arXiv:2403.09157</a> <span> [<a href="https://arxiv.org/pdf/2403.09157">pdf</a>, <a href="https://arxiv.org/ps/2403.09157">ps</a>, <a href="https://arxiv.org/format/2403.09157">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Mingya Zhang</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+Y">Yue Yu</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+L">Limei Gu</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+T">Tingsheng Lin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xianping Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.09157v1-abstract-short" style="display: inline;"> In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computational complexity poses a challenge for Transformers. Recently, State Space Models… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09157v1-abstract-full').style.display = 'inline'; document.getElementById('2403.09157v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.09157v1-abstract-full" style="display: none;"> In the field of medical image segmentation, models based on both CNN and Transformer have been thoroughly investigated. However, CNNs have limited modeling capabilities for long-range dependencies, making it challenging to exploit the semantic information within images fully. On the other hand, the quadratic computational complexity poses a challenge for Transformers. Recently, State Space Models (SSMs), such as Mamba, have been recognized as a promising method. They not only demonstrate superior performance in modeling long-range interactions, but also preserve a linear computational complexity. Inspired by the Mamba architecture, We proposed Vison Mamba-UNetV2, the Visual State Space (VSS) Block is introduced to capture extensive contextual information, the Semantics and Detail Infusion (SDI) is introduced to augment the infusion of low-level and high-level features. We conduct comprehensive experiments on the ISIC17, ISIC18, CVC-300, CVC-ClinicDB, Kvasir, CVC-ColonDB and ETIS-LaribPolypDB public datasets. The results indicate that VM-UNetV2 exhibits competitive performance in medical image segmentation tasks. Our code is available at https://github.com/nobodyplayer1/VM-UNetV2. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.09157v1-abstract-full').style.display = 'none'; document.getElementById('2403.09157v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.13073">arXiv:2402.13073</a> <span> [<a href="https://arxiv.org/pdf/2402.13073">pdf</a>, <a href="https://arxiv.org/format/2402.13073">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Towards Intelligent Communications: Large Model Empowered Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xie%2C+H">Huiqiang Xie</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.13073v2-abstract-short" style="display: inline;"> Deep learning enabled semantic communications have shown great potential to significantly improve transmission efficiency and alleviate spectrum scarcity, by effectively exchanging the semantics behind the data. Recently, the emergence of large models, boasting billions of parameters, has unveiled remarkable human-like intelligence, offering a promising avenue for advancing semantic communication… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13073v2-abstract-full').style.display = 'inline'; document.getElementById('2402.13073v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.13073v2-abstract-full" style="display: none;"> Deep learning enabled semantic communications have shown great potential to significantly improve transmission efficiency and alleviate spectrum scarcity, by effectively exchanging the semantics behind the data. Recently, the emergence of large models, boasting billions of parameters, has unveiled remarkable human-like intelligence, offering a promising avenue for advancing semantic communication by enhancing semantic understanding and contextual understanding. This article systematically investigates the large model-empowered semantic communication systems from potential applications to system design. First, we propose a new semantic communication architecture that seamlessly integrates large models into semantic communication through the introduction of a memory module. Then, the typical applications are illustrated to show the benefits of the new architecture. Besides, we discuss the key designs in implementing the new semantic communication systems from module design to system training. Finally, the potential research directions are identified to boost the large model-empowered semantic communications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13073v2-abstract-full').style.display = 'none'; document.getElementById('2402.13073v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.02950">arXiv:2402.02950</a> <span> [<a href="https://arxiv.org/pdf/2402.02950">pdf</a>, <a href="https://arxiv.org/format/2402.02950">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Semantic Entropy Can Simultaneously Benefit Transmission Efficiency and Channel Security of Wireless Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Rong%2C+Y">Yankai Rong</a>, <a href="/search/eess?searchtype=author&query=Nan%2C+G">Guoshun Nan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Minwei Zhang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+S">Sihan Chen</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Songtao Wang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xuefei Zhang</a>, <a href="/search/eess?searchtype=author&query=Ma%2C+N">Nan Ma</a>, <a href="/search/eess?searchtype=author&query=Gong%2C+S">Shixun Gong</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Z">Zhaohui Yang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+Q">Qimei Cui</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Quek%2C+T+Q+S">Tony Q. S. Quek</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.02950v3-abstract-short" style="display: inline;"> Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: "Can we further exploit the advantages of tra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02950v3-abstract-full').style.display = 'inline'; document.getElementById('2402.02950v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.02950v3-abstract-full" style="display: none;"> Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: "Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?". Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02950v3-abstract-full').style.display = 'none'; document.getElementById('2402.02950v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.17575">arXiv:2401.17575</a> <span> [<a href="https://arxiv.org/pdf/2401.17575">pdf</a>, <a href="https://arxiv.org/format/2401.17575">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Can We Improve Channel Reciprocity via Loop-back Compensation for RIS-assisted Physical Layer Key Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xu%2C+N">Ningya Xu</a>, <a href="/search/eess?searchtype=author&query=Nan%2C+G">Guoshun Nan</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+N">Na Li</a>, <a href="/search/eess?searchtype=author&query=Mao%2C+P">Pengxuan Mao</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+T">Tianyuan Yang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.17575v3-abstract-short" style="display: inline;"> Reconfigurable intelligent surface (RIS) facilitates the extraction of unpredictable channel features for physical layer key generation (PKG), securing communications among legitimate users with symmetric keys. Previous works have demonstrated that channel reciprocity plays a crucial role in generating symmetric keys in PKG systems, whereas, in reality, reciprocity is greatly affected by hardware… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.17575v3-abstract-full').style.display = 'inline'; document.getElementById('2401.17575v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.17575v3-abstract-full" style="display: none;"> Reconfigurable intelligent surface (RIS) facilitates the extraction of unpredictable channel features for physical layer key generation (PKG), securing communications among legitimate users with symmetric keys. Previous works have demonstrated that channel reciprocity plays a crucial role in generating symmetric keys in PKG systems, whereas, in reality, reciprocity is greatly affected by hardware interference and RIS-based jamming attacks. This motivates us to propose LoCKey, a novel approach that aims to improve channel reciprocity by mitigating interferences and attacks with a loop-back compensation scheme, thus maximizing the secrecy performance of the PKG system. Specifically, our proposed LoCKey is capable of effectively compensating for the CSI non-reciprocity by the combination of transmit-back signal value and error minimization module. Firstly, we introduce the entire flowchart of our method and provide an in-depth discussion of each step. Following that, we delve into a theoretical analysis of the performance optimizations when our LoCKey is applied for CSI reciprocity enhancement. Finally, we conduct experiments to verify the effectiveness of the proposed LoCKey in improving channel reciprocity under various interferences for RIS-assisted wireless communications. The results demonstrate a significant improvement in both the rate of key generation assisted by the RIS and the consistency of the generated keys, showing great potential for the practical deployment of our LoCKey in future wireless systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.17575v3-abstract-full').style.display = 'none'; document.getElementById('2401.17575v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by ICC 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.00859">arXiv:2401.00859</a> <span> [<a href="https://arxiv.org/pdf/2401.00859">pdf</a>, <a href="https://arxiv.org/ps/2401.00859">ps</a>, <a href="https://arxiv.org/format/2401.00859">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Federated Multi-View Synthesizing for Metaverse </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Guo%2C+Y">Yiyu Guo</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+G+Y">Geoffrey Ye Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.00859v1-abstract-short" style="display: inline;"> The metaverse is expected to provide immersive entertainment, education, and business applications. However, virtual reality (VR) transmission over wireless networks is data- and computation-intensive, making it critical to introduce novel solutions that meet stringent quality-of-service requirements. With recent advances in edge intelligence and deep learning, we have developed a novel multi-view… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.00859v1-abstract-full').style.display = 'inline'; document.getElementById('2401.00859v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.00859v1-abstract-full" style="display: none;"> The metaverse is expected to provide immersive entertainment, education, and business applications. However, virtual reality (VR) transmission over wireless networks is data- and computation-intensive, making it critical to introduce novel solutions that meet stringent quality-of-service requirements. With recent advances in edge intelligence and deep learning, we have developed a novel multi-view synthesizing framework that can efficiently provide computation, storage, and communication resources for wireless content delivery in the metaverse. We propose a three-dimensional (3D)-aware generative model that uses collections of single-view images. These single-view images are transmitted to a group of users with overlapping fields of view, which avoids massive content transmission compared to transmitting tiles or whole 3D models. We then present a federated learning approach to guarantee an efficient learning process. The training performance can be improved by characterizing the vertical and horizontal data samples with a large latent feature space, while low-latency communication can be achieved with a reduced number of transmitted parameters during federated learning. We also propose a federated transfer learning framework to enable fast domain adaptation to different target domains. Simulation results have demonstrated the effectiveness of our proposed federated multi-view synthesizing framework for VR content delivery. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.00859v1-abstract-full').style.display = 'none'; document.getElementById('2401.00859v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.04685">arXiv:2311.04685</a> <span> [<a href="https://arxiv.org/pdf/2311.04685">pdf</a>, <a href="https://arxiv.org/format/2311.04685">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> An End-Cloud Computing Enabled Surveillance Video Transmission System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yang%2C+D">Dingxi Yang</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+L">Liting Wang</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+F">Fang Cui</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+H">Hengjiang Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.04685v1-abstract-short" style="display: inline;"> The enormous data volume of video poses a significant burden on the network. Particularly, transferring high-definition surveillance videos to the cloud consumes a significant amount of spectrum resources. To address these issues, we propose a surveillance video transmission system enabled by end-cloud computing. Specifically, the cameras actively down-sample the original video and then a redundan… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.04685v1-abstract-full').style.display = 'inline'; document.getElementById('2311.04685v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.04685v1-abstract-full" style="display: none;"> The enormous data volume of video poses a significant burden on the network. Particularly, transferring high-definition surveillance videos to the cloud consumes a significant amount of spectrum resources. To address these issues, we propose a surveillance video transmission system enabled by end-cloud computing. Specifically, the cameras actively down-sample the original video and then a redundant frame elimination module is employed to further reduce the data volume of surveillance videos. Then we develop a key-frame assisted video super-resolution model to reconstruct the high-quality video at the cloud side. Moreover, we propose a strategy of extracting key frames from source videos for better reconstruction performance by utilizing the peak signal-to-noise ratio (PSNR) of adjacent frames to measure the propagation distance of key frame information. Simulation results show that the developed system can effectively reduce the data volume by the end-cloud collaboration and outperforms existing video super-resolution models significantly in terms of PSNR and structural similarity index (SSIM). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.04685v1-abstract-full').style.display = 'none'; document.getElementById('2311.04685v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.02653">arXiv:2309.02653</a> <span> [<a href="https://arxiv.org/pdf/2309.02653">pdf</a>, <a href="https://arxiv.org/format/2309.02653">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Passive Eavesdropping Can Significantly Slow Down RIS-Assisted Secret Key Generation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xu%2C+N">Ningya Xu</a>, <a href="/search/eess?searchtype=author&query=Nan%2C+G">Guoshun Nan</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.02653v2-abstract-short" style="display: inline;"> Reconfigurable Intelligent Surface (RIS) assisted physical layer key generation has shown great potential to secure wireless communications by smartly controlling signals such as phase and amplitude. However, previous studies mainly focus on RIS adjustment under ideal conditions, while the correlation between the eavesdropping channel and the legitimate channel, a more practical setting in the rea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.02653v2-abstract-full').style.display = 'inline'; document.getElementById('2309.02653v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.02653v2-abstract-full" style="display: none;"> Reconfigurable Intelligent Surface (RIS) assisted physical layer key generation has shown great potential to secure wireless communications by smartly controlling signals such as phase and amplitude. However, previous studies mainly focus on RIS adjustment under ideal conditions, while the correlation between the eavesdropping channel and the legitimate channel, a more practical setting in the real world, is still largely under-explored for the key generation. To fill this gap, this paper aims to maximize the RIS-assisted physical-layer secret key generation by optimizing the RIS units switching under the eavesdropping channel. Firstly, we theoretically show that passive eavesdropping significantly reduces RIS-assisted secret key generation. Keeping this in mind, we then introduce a mathematical formulation to maximize the key generation rate and provide a step-by-step analysis. Extensive experiments show the effectiveness of our method in benefiting the secret key capacity under the eavesdropping channel. We also observe that the key randomness, and unmatched key rate, two metrics that measure the secret key quality, are also significantly improved, potentially paving the way to RIS-assisted key generation in real-world scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.02653v2-abstract-full').style.display = 'none'; document.getElementById('2309.02653v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by Globecom 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.05760">arXiv:2308.05760</a> <span> [<a href="https://arxiv.org/pdf/2308.05760">pdf</a>, <a href="https://arxiv.org/format/2308.05760">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> </div> </div> <p class="title is-5 mathjax"> Unified Statistical Channel Modeling and performance analysis of Vertical Underwater Wireless Optical Communication Links considering Turbulence-Induced Fading </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xu%2C+D">Dongling Xu</a>, <a href="/search/eess?searchtype=author&query=Yi%2C+X">Xiang Yi</a>, <a href="/search/eess?searchtype=author&query=Ata%2C+Y">Yal莽n Ata</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xinyue Tao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yuxuan Li</a>, <a href="/search/eess?searchtype=author&query=Yue%2C+P">Peng Yue</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.05760v1-abstract-short" style="display: inline;"> The reliability of a vertical underwater wireless optical communication (UWOC) network is seriously impacted by turbulence-induced fading due to fluctuations in the water temperature and salinity, which vary with depth. To better assess the vertical UWOC system performances, an accurate probability distribution function (PDF) model that can describe this fading is indispensable. In view of the lim… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05760v1-abstract-full').style.display = 'inline'; document.getElementById('2308.05760v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.05760v1-abstract-full" style="display: none;"> The reliability of a vertical underwater wireless optical communication (UWOC) network is seriously impacted by turbulence-induced fading due to fluctuations in the water temperature and salinity, which vary with depth. To better assess the vertical UWOC system performances, an accurate probability distribution function (PDF) model that can describe this fading is indispensable. In view of the limitations of theoretical and experimental studies, this paper is the first to establish a more accurate modeling scheme for wave optics simulation (WOS) by fully considering the constraints of sampling conditions on multi-phase screen parameters. On this basis, we complete the modeling of light propagation in a vertical oceanic turbulence channel and subsequently propose a unified statistical model named mixture Weibull-generalized Gamma (WGG) distribution model to characterize turbulence-induced fading in vertical links. Interestingly, the WGG model is shown to provide a perfect fit with the acquired data under all considered channel conditions. We further show that the application of the WGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the average bit-error rate (BER). The presented results give valuable insight into the practical aspects of development of UWOC networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05760v1-abstract-full').style.display = 'none'; document.getElementById('2308.05760v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.02900">arXiv:2307.02900</a> <span> [<a href="https://arxiv.org/pdf/2307.02900">pdf</a>, <a href="https://arxiv.org/ps/2307.02900">ps</a>, <a href="https://arxiv.org/format/2307.02900">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TWC.2023.3345363">10.1109/TWC.2023.3345363 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Meta Federated Reinforcement Learning for Distributed Resource Allocation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+Z">Zelin Ji</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2307.02900v2-abstract-short" style="display: inline;"> In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper explores a distributed resource allocation method that aims to maximize energy efficiency (EE) while ensuring the quality of service (QoS) for users. Specifically, in order to address wireless channel condi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.02900v2-abstract-full').style.display = 'inline'; document.getElementById('2307.02900v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.02900v2-abstract-full" style="display: none;"> In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper explores a distributed resource allocation method that aims to maximize energy efficiency (EE) while ensuring the quality of service (QoS) for users. Specifically, in order to address wireless channel conditions, we propose a robust meta federated reinforcement learning (\textit{MFRL}) framework that allows local users to optimize transmit power and assign channels using locally trained neural network models, so as to offload computational burden from the cloud server to the local users, reducing transmission overhead associated with local channel state information. The BS performs the meta learning procedure to initialize a general global model, enabling rapid adaptation to different environments with improved EE performance. The federated learning technique, based on decentralized reinforcement learning, promotes collaboration and mutual benefits among users. Analysis and numerical results demonstrate that the proposed \textit{MFRL} framework accelerates the reinforcement learning process, decreases transmission overhead, and offloads computation, while outperforming the conventional decentralized reinforcement learning algorithm in terms of convergence speed and EE performance across various scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.02900v2-abstract-full').style.display = 'none'; document.getElementById('2307.02900v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to TWC</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.08247">arXiv:2305.08247</a> <span> [<a href="https://arxiv.org/pdf/2305.08247">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> A Fast and Robust Camera-IMU Online Calibration Method For Localization System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaowen Tao</a>, <a href="/search/eess?searchtype=author&query=Meng%2C+P">Pengxiang Meng</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+B">Bing Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+J">Jian Zhao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.08247v1-abstract-short" style="display: inline;"> Autonomous driving has spurred the development of sensor fusion techniques, which combine data from multiple sensors to improve system performance. In particular, localization system based on sensor fusion , such as Visual Simultaneous Localization and Mapping (VSLAM), is an important component in environment perception, and is the basis of decision-making and motion control for intelligent vehicl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.08247v1-abstract-full').style.display = 'inline'; document.getElementById('2305.08247v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.08247v1-abstract-full" style="display: none;"> Autonomous driving has spurred the development of sensor fusion techniques, which combine data from multiple sensors to improve system performance. In particular, localization system based on sensor fusion , such as Visual Simultaneous Localization and Mapping (VSLAM), is an important component in environment perception, and is the basis of decision-making and motion control for intelligent vehicles. The accuracy of extrinsic calibration parameters between camera and IMU has significant effect on the positioning precision when performing VSLAM system. Currently, existing methods are time-consuming using complex optimization methods and sensitive to noise and outliers due to off-calibration, which can negatively impact system performance. To address these problems, this paper presents a fast and robust camera-IMU online calibration method based space coordinate transformation constraints and SVD (singular Value Decomposition) tricks. First, constraint equations are constructed based on equality of rotation and transformation matrices between camera frames and IMU coordinates at different moments. Secondly, the external parameters of the camera-IMU are solved using quaternion transformation and SVD techniques. Finally, the proposed method is validated using ROS platform, where images from the camera and velocity, acceleration, and angular velocity data from the IMU are recorded in a ROS bag file. The results showed that the proposed method can achieve robust and reliable camera-IMU online calibration parameters results with less tune consuming and less uncertainty. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.08247v1-abstract-full').style.display = 'none'; document.getElementById('2305.08247v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.07220">arXiv:2305.07220</a> <span> [<a href="https://arxiv.org/pdf/2305.07220">pdf</a>, <a href="https://arxiv.org/format/2305.07220">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Nan%2C+G">Guoshun Nan</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Z">Zhichun Li</a>, <a href="/search/eess?searchtype=author&query=Zhai%2C+J">Jinli Zhai</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+Q">Qimei Cui</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+G">Gong Chen</a>, <a href="/search/eess?searchtype=author&query=Du%2C+X">Xin Du</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xuefei Zhang</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Quek%2C+T+Q+S">Tony Q. S. Quek</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.07220v1-abstract-short" style="display: inline;"> End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driv… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.07220v1-abstract-full').style.display = 'inline'; document.getElementById('2305.07220v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.07220v1-abstract-full" style="display: none;"> End-to-end semantic communications (ESC) rely on deep neural networks (DNN) to boost communication efficiency by only transmitting the semantics of data, showing great potential for high-demand mobile applications. We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare. However, robustifying semantic interpretation is challenging as ESC is extremely vulnerable to physical-layer adversarial attacks due to the openness of wireless channels and the fragileness of neural models. Toward ESC robustness in practice, we ask the following two questions: Q1: For attacks, is it possible to generate semantic-oriented physical-layer adversarial attacks that are imperceptible, input-agnostic and controllable? Q2: Can we develop a defense strategy against such semantic distortions and previously proposed adversaries? To this end, we first present MobileSC, a novel semantic communication framework that considers the computation and memory efficiency in wireless environments. Equipped with this framework, we propose SemAdv, a physical-layer adversarial perturbation generator that aims to craft semantic adversaries over the air with the abovementioned criteria, thus answering the Q1. To better characterize the realworld effects for robust training and evaluation, we further introduce a novel adversarial training method SemMixed to harden the ESC against SemAdv attacks and existing strong threats, thus answering the Q2. Extensive experiments on three public benchmarks verify the effectiveness of our proposed methods against various physical adversarial attacks. We also show some interesting findings, e.g., our MobileSC can even be more robust than classical block-wise communication systems in the low SNR regime. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.07220v1-abstract-full').style.display = 'none'; document.getElementById('2305.07220v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 28 figures, accepted by IEEE jsac</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.06543">arXiv:2305.06543</a> <span> [<a href="https://arxiv.org/pdf/2305.06543">pdf</a>, <a href="https://arxiv.org/format/2305.06543">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> QoE-based Semantic-Aware Resource Allocation for Multi-Task Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yan%2C+L">Lei Yan</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Li%2C+C">Chunfeng Li</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rui Zhang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yongzhao Li</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.06543v2-abstract-short" style="display: inline;"> By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic theory about semantic information quantification and performance evaluation makes it challenging to perform resource allocation for semantic communications, especially when multiple tasks coexist in the network. To cope wi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.06543v2-abstract-full').style.display = 'inline'; document.getElementById('2305.06543v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.06543v2-abstract-full" style="display: none;"> By transmitting task-related information only, semantic communications yield significant performance gains over conventional communications. However, the lack of mature semantic theory about semantic information quantification and performance evaluation makes it challenging to perform resource allocation for semantic communications, especially when multiple tasks coexist in the network. To cope with this challenge, we propose a quality-of-experience (QoE) based semantic-aware resource allocation method for multi-task networks in this paper. First, semantic entropy is defined to quantify the semantic information for different tasks, and the relationship between semantic entropy and Shannon entropy is analyzed. Then, we develop a novel QoE model to formulate the semantic-aware resource allocation in terms of semantic compression, channel assignment, and transmit power. The compatibility of the formulated problem with conventional communications is further demonstrated. To solve this problem, we decouple it into two subproblems and solved them by a developed deep Q-network (DQN) based method and a proposed low-complexity matching algorithm, respectively. Finally, simulation results validate the effectiveness and superiority of the proposed method, as well as its compatibility with conventional communications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.06543v2-abstract-full').style.display = 'none'; document.getElementById('2305.06543v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This work has been accepted by IEEE Transactions on Wireless Communications. arXiv admin note: text overlap with arXiv:2205.14530</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.16523">arXiv:2303.16523</a> <span> [<a href="https://arxiv.org/pdf/2303.16523">pdf</a>, <a href="https://arxiv.org/format/2303.16523">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Boosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+Z">Zeju Li</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+X">Xinghan Liu</a>, <a href="/search/eess?searchtype=author&query=Nan%2C+G">Guoshun Nan</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+J">Jinfei Zhou</a>, <a href="/search/eess?searchtype=author&query=Lyu%2C+X">Xinchen Lyu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+Q">Qimei Cui</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaofeng Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2303.16523v2-abstract-short" style="display: inline;"> End-to-end semantic communication (ESC) system is able to improve communication efficiency by only transmitting the semantics of the input rather than raw bits. Although promising, ESC has also been shown susceptible to the crafted physical layer adversarial perturbations due to the openness of wireless channels and the sensitivity of neural models. Previous works focus more on the physical layer… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.16523v2-abstract-full').style.display = 'inline'; document.getElementById('2303.16523v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.16523v2-abstract-full" style="display: none;"> End-to-end semantic communication (ESC) system is able to improve communication efficiency by only transmitting the semantics of the input rather than raw bits. Although promising, ESC has also been shown susceptible to the crafted physical layer adversarial perturbations due to the openness of wireless channels and the sensitivity of neural models. Previous works focus more on the physical layer white-box attacks, while the challenging black-box ones, as more practical adversaries in real-world cases, are still largely under-explored. To this end, we present SemBLK, a novel method that can learn to generate destructive physical layer semantic attacks for an ESC system under the black-box setting, where the adversaries are imperceptible to humans. Specifically, 1) we first introduce a surrogate semantic encoder and train its parameters by exploring a limited number of queries to an existing ESC system. 2) Equipped with such a surrogate encoder, we then propose a novel semantic perturbation generation method to learn to boost the physical layer attacks with semantic adversaries. Experiments on two public datasets show the effectiveness of our proposed SemBLK in attacking the ESC system under the black-box setting. Finally, we provide case studies to visually justify the superiority of our physical layer semantic perturbations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.16523v2-abstract-full').style.display = 'none'; document.getElementById('2303.16523v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted by ICC2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.08376">arXiv:2301.08376</a> <span> [<a href="https://arxiv.org/pdf/2301.08376">pdf</a>, <a href="https://arxiv.org/ps/2301.08376">ps</a>, <a href="https://arxiv.org/format/2301.08376">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/TWC.2024.3390407">10.1109/TWC.2024.3390407 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Resource Optimization for Semantic-Aware Networks with Task Offloading </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ji%2C+Z">Zelin Ji</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+H">Han Zhu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.08376v2-abstract-short" style="display: inline;"> The limited capabilities of user equipment restrict the local implementation of computation-intensive applications. Edge computing, especially the edge intelligence system, enables local users to offload the computation tasks to the edge servers to reduce the computational energy consumption of user equipment and accelerate fast task execution. However, the limited bandwidth of upstream channels m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.08376v2-abstract-full').style.display = 'inline'; document.getElementById('2301.08376v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.08376v2-abstract-full" style="display: none;"> The limited capabilities of user equipment restrict the local implementation of computation-intensive applications. Edge computing, especially the edge intelligence system, enables local users to offload the computation tasks to the edge servers to reduce the computational energy consumption of user equipment and accelerate fast task execution. However, the limited bandwidth of upstream channels may increase the task transmission latency and affect the computation offloading performance. To overcome the challenge arising from scarce wireless communication resources, we propose a semantic-aware multi-modal task offloading system that facilitates the extraction and offloading of semantic task information to edge servers. To cope with the different tasks with multi-modal data, a unified quality of experience (QoE) criterion is designed. Furthermore, a proximal policy optimization-based multi-agent reinforcement learning algorithm (MAPPO) is proposed to coordinate the resource management for wireless communications and computation in a distributed and low computational complexity manner. Simulation results verify that the proposed MAPPO algorithm outperforms other reinforcement learning algorithms and fixed schemes in terms of task execution speed and the overall system QoE. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.08376v2-abstract-full').style.display = 'none'; document.getElementById('2301.08376v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to TWC, in major revision</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.05837">arXiv:2301.05837</a> <span> [<a href="https://arxiv.org/pdf/2301.05837">pdf</a>, <a href="https://arxiv.org/ps/2301.05837">ps</a>, <a href="https://arxiv.org/format/2301.05837">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yang%2C+Y">Yuwen Yang</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Feifei Gao</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+G">Guangyi Liu</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+C">Chengkang Pan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.05837v1-abstract-short" style="display: inline;"> In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks. As a case study, we develop an environment semantics ai… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.05837v1-abstract-full').style.display = 'inline'; document.getElementById('2301.05837v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.05837v1-abstract-full" style="display: none;"> In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks. As a case study, we develop an environment semantics aided network architecture for mmWave communication systems, which is composed of a semantic feature extraction network, a feature selection algorithm, a task-oriented encoder, and a decision network. With images taken from street cameras and user's identification information as the inputs, the environment semantics aided network architecture is trained to predict the optimal beam index and the blockage state for the base station. It is seen that without pilot training or the costly beam scans, the environment semantics aided network architecture can realize extremely efficient beam prediction and timely blockage prediction, thus meeting requirements for ultra-reliable and low-latency communications (URLLCs). Simulation results demonstrate that compared with existing works, the proposed environment semantics aided network architecture can reduce system overheads such as storage space and computational cost while achieving satisfactory prediction accuracy and protecting user privacy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.05837v1-abstract-full').style.display = 'none'; document.getElementById('2301.05837v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.04552">arXiv:2301.04552</a> <span> [<a href="https://arxiv.org/pdf/2301.04552">pdf</a>, <a href="https://arxiv.org/format/2301.04552">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Generalized Semantic Communication System: from Sources to Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Feifei Gao</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+B">Bo Lin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+G">Guangyi Liu</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+C">Chengkang Pan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.04552v2-abstract-short" style="display: inline;"> Semantic communication is regarded as the breakthrough beyond the Shannon paradigm, which transmits only semantic information to significantly improve communication efficiency. This article introduces a framework for generalized semantic communication system, which exploits the semantic information in both the multimodal source and the wireless channel environment. Subsequently, the developed deep… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04552v2-abstract-full').style.display = 'inline'; document.getElementById('2301.04552v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.04552v2-abstract-full" style="display: none;"> Semantic communication is regarded as the breakthrough beyond the Shannon paradigm, which transmits only semantic information to significantly improve communication efficiency. This article introduces a framework for generalized semantic communication system, which exploits the semantic information in both the multimodal source and the wireless channel environment. Subsequently, the developed deep learning enabled end-to-end semantic communication and environment semantics aided wireless communication techniques are demonstrated through two examples. The article concludes with several research challenges to boost the development of such a generalized semantic communication system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04552v2-abstract-full').style.display = 'none'; document.getElementById('2301.04552v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.11519">arXiv:2209.11519</a> <span> [<a href="https://arxiv.org/pdf/2209.11519">pdf</a>, <a href="https://arxiv.org/format/2209.11519">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/LWC.2023.3255221">10.1109/LWC.2023.3255221 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Vector Quantized Semantic Communication System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fu%2C+Q">Qifan Fu</a>, <a href="/search/eess?searchtype=author&query=Xie%2C+H">Huiqiang Xie</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Slabaugh%2C+G">Gregory Slabaugh</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2209.11519v2-abstract-short" style="display: inline;"> Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems. In this paper, we develop a deep learning (DL)-enabled vector quantized (VQ) semantic communication system for image transmission, named VQ-DeepSC. Specifically, we propose a convolutional neural network (CNN)-based transceiver to extr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.11519v2-abstract-full').style.display = 'inline'; document.getElementById('2209.11519v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.11519v2-abstract-full" style="display: none;"> Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems. In this paper, we develop a deep learning (DL)-enabled vector quantized (VQ) semantic communication system for image transmission, named VQ-DeepSC. Specifically, we propose a convolutional neural network (CNN)-based transceiver to extract multi-scale semantic features of images and introduce multi-scale semantic embedding spaces to perform semantic feature quantization, rendering the data compatible with digital communication systems. Furthermore, we employ adversarial training to improve the quality of received images by introducing a PatchGAN discriminator. Experimental results demonstrate that the proposed VQ-DeepSC is more robustness than BPG in digital communication systems and has comparable MS-SSIM performance to the DeepJSCC method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.11519v2-abstract-full').style.display = 'none'; document.getElementById('2209.11519v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This five pages article has been accepted for publication in IEEE Wireless Communications Letters. This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/LWC.2023.3255221</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.07689">arXiv:2209.07689</a> <span> [<a href="https://arxiv.org/pdf/2209.07689">pdf</a>, <a href="https://arxiv.org/format/2209.07689">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Unified Multi-Task Semantic Communication System for Multimodal Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+G">Guangyi Zhang</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+Q">Qiyu Hu</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Cai%2C+Y">Yunlong Cai</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+G">Guanding Yu</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2209.07689v3-abstract-short" style="display: inline;"> Task-oriented semantic communications have achieved significant performance gains. However, the employed deep neural networks in semantic communications have to be updated when the task is changed or multiple models need to be stored for performing different tasks. To address this issue, we develop a unified deep learning-enabled semantic communication system (U-DeepSC), where a unified end-to-end… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.07689v3-abstract-full').style.display = 'inline'; document.getElementById('2209.07689v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.07689v3-abstract-full" style="display: none;"> Task-oriented semantic communications have achieved significant performance gains. However, the employed deep neural networks in semantic communications have to be updated when the task is changed or multiple models need to be stored for performing different tasks. To address this issue, we develop a unified deep learning-enabled semantic communication system (U-DeepSC), where a unified end-to-end framework can serve many different tasks with multiple modalities of data. As the number of required features varies from task to task, we propose a vector-wise dynamic scheme that can adjust the number of transmitted symbols for different tasks. Moreover, our dynamic scheme can also adaptively adjust the number of transmitted features under different channel conditions to optimize the transmission efficiency. Particularly, we devise a lightweight feature selection module (FSM) to evaluate the importance of feature vectors, which can hierarchically drop redundant feature vectors and significantly accelerate the inference. To reduce the transmission overhead, we then design a unified codebook for feature representation to serve multiple tasks, where only the indices of these task-specific features in the codebook are transmitted. According to the simulation results, the proposed U-DeepSC achieves comparable performance to the task-oriented semantic communication system designed for a specific task but with significant reduction in both transmission overhead and model size. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.07689v3-abstract-full').style.display = 'none'; document.getElementById('2209.07689v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.11409">arXiv:2207.11409</a> <span> [<a href="https://arxiv.org/pdf/2207.11409">pdf</a>, <a href="https://arxiv.org/format/2207.11409">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Computer Vision Aided mmWave Beam Alignment in V2X Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xu%2C+W">Weihua Xu</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Feifei Gao</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jianhua Zhang</a>, <a href="/search/eess?searchtype=author&query=Alkhateeb%2C+A">Ahmed Alkhateeb</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2207.11409v1-abstract-short" style="display: inline;"> Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.11409v1-abstract-full').style.display = 'inline'; document.getElementById('2207.11409v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.11409v1-abstract-full" style="display: none;"> Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.11409v1-abstract-full').style.display = 'none'; document.getElementById('2207.11409v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">32 pages, 16 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.02596">arXiv:2206.02596</a> <span> [<a href="https://arxiv.org/pdf/2206.02596">pdf</a>, <a href="https://arxiv.org/format/2206.02596">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Robust Deep Learning Enabled Semantic Communication System for Text </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Peng%2C+X">Xiang Peng</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+D">Danlan Huang</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+J">Jianhua Lu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+G">Guangyi Liu</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+C">Chengkang Pan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2206.02596v1-abstract-short" style="display: inline;"> With the advent of the 6G era, the concept of semantic communication has attracted increasing attention. Compared with conventional communication systems, semantic communication systems are not only affected by physical noise existing in the wireless communication environment, e.g., additional white Gaussian noise, but also by semantic noise due to the source and the nature of deep learning-based… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.02596v1-abstract-full').style.display = 'inline'; document.getElementById('2206.02596v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.02596v1-abstract-full" style="display: none;"> With the advent of the 6G era, the concept of semantic communication has attracted increasing attention. Compared with conventional communication systems, semantic communication systems are not only affected by physical noise existing in the wireless communication environment, e.g., additional white Gaussian noise, but also by semantic noise due to the source and the nature of deep learning-based systems. In this paper, we elaborate on the mechanism of semantic noise. In particular, we categorize semantic noise into two categories: literal semantic noise and adversarial semantic noise. The former is caused by written errors or expression ambiguity, while the latter is caused by perturbations or attacks added to the embedding layer via the semantic channel. To prevent semantic noise from influencing semantic communication systems, we present a robust deep learning enabled semantic communication system (R-DeepSC) that leverages a calibrated self-attention mechanism and adversarial training to tackle semantic noise. Compared with baseline models that only consider physical noise for text transmission, the proposed R-DeepSC achieves remarkable performance in dealing with semantic noise under different signal-to-noise ratios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.02596v1-abstract-full').style.display = 'none'; document.getElementById('2206.02596v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.14070">arXiv:2205.14070</a> <span> [<a href="https://arxiv.org/pdf/2205.14070">pdf</a>, <a href="https://arxiv.org/format/2205.14070">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Multi-criteria Decision-making of Intelligent Vehicles under Fault Condition Enhancing Public-private Partnership </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xin Tao</a>, <a href="/search/eess?searchtype=author&query=%C4%8Ci%C4%8Di%C4%87%2C+M">Mladen 膶i膷i膰</a>, <a href="/search/eess?searchtype=author&query=M%C3%A5rtensson%2C+J">Jonas M氓rtensson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.14070v1-abstract-short" style="display: inline;"> With the development of vehicular technologies on automation, electrification, and digitalization, vehicles are becoming more intelligent while being exposed to more complex, uncertain, and frequently occurring faults. In this paper, we look into the maintenance planning of an operating vehicle under fault condition and formulate it as a multi-criteria decision-making problem. The maintenance deci… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.14070v1-abstract-full').style.display = 'inline'; document.getElementById('2205.14070v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.14070v1-abstract-full" style="display: none;"> With the development of vehicular technologies on automation, electrification, and digitalization, vehicles are becoming more intelligent while being exposed to more complex, uncertain, and frequently occurring faults. In this paper, we look into the maintenance planning of an operating vehicle under fault condition and formulate it as a multi-criteria decision-making problem. The maintenance decisions are generated by route searching in road networks and evaluated based on risk assessment considering the uncertainty of vehicle breakdowns. Particularly, we consider two criteria, namely the risk of public time loss and the risk of mission delay, representing the concerns of the public sector and the private sector, respectively. A public time loss model is developed to evaluate the traffic congestion caused by a vehicle breakdown and the corresponding towing process. The Pareto optimal set of non-dominated decisions is derived by evaluating the risk of the decisions. We demonstrate the relevance of the problem and the effectiveness of the proposed method by numerical experiments derived from real-world scenarios. The experiments show that neglecting the risk of vehicle breakdown on public roads can cause a high risk of public time loss in dense traffic flow. With the proposed method, alternate decisions can be derived to reduce the risks of public time loss significantly with a low increase in the risk of mission delay. This study aims at catalyzing public-private partnership through collaborative decision-making between the private sector and the public sector, thus archiving a more sustainable transportation system in the future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.14070v1-abstract-full').style.display = 'none'; document.getElementById('2205.14070v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.04603">arXiv:2205.04603</a> <span> [<a href="https://arxiv.org/pdf/2205.04603">pdf</a>, <a href="https://arxiv.org/format/2205.04603">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Deep Learning Enabled Semantic Communications with Speech Recognition and Synthesis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Weng%2C+Z">Zhenzi Weng</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+C">Chengkang Pan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+G">Guangyi Liu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+G+Y">Geoffrey Ye Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2205.04603v2-abstract-short" style="display: inline;"> In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively. First, the speech recognition-related semantic features are extracted for transmission by a joint semantic-channel encoder and the text is recovered at the rec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.04603v2-abstract-full').style.display = 'inline'; document.getElementById('2205.04603v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.04603v2-abstract-full" style="display: none;"> In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively. First, the speech recognition-related semantic features are extracted for transmission by a joint semantic-channel encoder and the text is recovered at the receiver based on the received semantic features, which significantly reduces the required amount of data transmission without performance degradation. Then, we perform speech synthesis at the receiver, which dedicates to re-generate the speech signals by feeding the recognized text and the speaker information into a neural network module. To enable the DeepSC-ST adaptive to dynamic channel environments, we identify a robust model to cope with different channel conditions. According to the simulation results, the proposed DeepSC-ST significantly outperforms conventional communication systems and existing DL-enabled communication systems, especially in the low signal-to-noise ratio (SNR) regime. A software demonstration is further developed as a proof-of-concept of the DeepSC-ST. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.04603v2-abstract-full').style.display = 'none'; document.getElementById('2205.04603v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:2107.11190</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.04554">arXiv:2202.04554</a> <span> [<a href="https://arxiv.org/pdf/2202.04554">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Real-time decision-making for autonomous vehicles under faults </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xin Tao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+Z">Zhao Yuan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2202.04554v1-abstract-short" style="display: inline;"> This paper addresses the challenges of decision-making for autonomous vehicles under faults during a transport mission. A real-time decision-making problem of vehicle routing planning considering maintenance management is formulated as an optimization problem. The goal is to minimize the total time to finish the transport mission by selecting the optimal workshop to conduct the maintenance and the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04554v1-abstract-full').style.display = 'inline'; document.getElementById('2202.04554v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.04554v1-abstract-full" style="display: none;"> This paper addresses the challenges of decision-making for autonomous vehicles under faults during a transport mission. A real-time decision-making problem of vehicle routing planning considering maintenance management is formulated as an optimization problem. The goal is to minimize the total time to finish the transport mission by selecting the optimal workshop to conduct the maintenance and the corresponding routes. Two methods are proposed to solve the optimization problem based on two methods of fundamental solutions: (1) Mixed Integer Programming; (2) Dijkstra's algorithm. We adapt these methods to solve the optimization problem and consider improving the computation efficiency. Numerical studies of test cases of highway and urban scenarios are presented to demonstrate the proposed methods, which show the feasibility and high computational efficiency of both methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.04554v1-abstract-full').style.display = 'none'; document.getElementById('2202.04554v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by the IEEE 9th International Conference on Industrial Engineering and Applications (ICIEA 2022). Date: November 2021. Email: taoxin@kth.se, zhaoyuan@hi.is, zhaoyuan.epslab@gmail.com</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.01389">arXiv:2201.01389</a> <span> [<a href="https://arxiv.org/pdf/2201.01389">pdf</a>, <a href="https://arxiv.org/format/2201.01389">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Semantic Communications: Principles and Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+J">Jianhua Lu</a>, <a href="/search/eess?searchtype=author&query=Tong%2C+W">Wen Tong</a>, <a href="/search/eess?searchtype=author&query=Li%2C+G+Y">Geoffrey Ye Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2201.01389v5-abstract-short" style="display: inline;"> Semantic communication, regarded as the breakthrough beyond the Shannon paradigm, aims at the successful transmission of semantic information conveyed by the source rather than the accurate reception of each single symbol or bit regardless of its meaning. This article provides an overview on semantic communications. After a brief review of Shannon information theory, we discuss semantic communicat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.01389v5-abstract-full').style.display = 'inline'; document.getElementById('2201.01389v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.01389v5-abstract-full" style="display: none;"> Semantic communication, regarded as the breakthrough beyond the Shannon paradigm, aims at the successful transmission of semantic information conveyed by the source rather than the accurate reception of each single symbol or bit regardless of its meaning. This article provides an overview on semantic communications. After a brief review of Shannon information theory, we discuss semantic communications with theory, framework, and system design enabled by deep learning. Different from the symbol/bit error rate used for measuring conventional communication systems, performance metrics for semantic communications are also discussed. The article concludes with several open questions in semantic communications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.01389v5-abstract-full').style.display = 'none'; document.getElementById('2201.01389v5-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.10255">arXiv:2112.10255</a> <span> [<a href="https://arxiv.org/pdf/2112.10255">pdf</a>, <a href="https://arxiv.org/format/2112.10255">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Task-Oriented Multi-User Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xie%2C+H">Huiqiang Xie</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2112.10255v1-abstract-short" style="display: inline;"> While semantic communications have shown the potential in the case of single-modal single-users, its applications to the multi-user scenario remain limited. In this paper, we investigate deep learning (DL) based multi-user semantic communication systems for transmitting single-modal data and multimodal data, respectively. We will adopt three intelligent tasks, including, image retrieval, machine t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.10255v1-abstract-full').style.display = 'inline'; document.getElementById('2112.10255v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.10255v1-abstract-full" style="display: none;"> While semantic communications have shown the potential in the case of single-modal single-users, its applications to the multi-user scenario remain limited. In this paper, we investigate deep learning (DL) based multi-user semantic communication systems for transmitting single-modal data and multimodal data, respectively. We will adopt three intelligent tasks, including, image retrieval, machine translation, and visual question answering (VQA) as the transmission goal of semantic communication systems. We will then propose a Transformer based unique framework to unify the structure of transmitters for different tasks. For the single-modal multi-user system, we will propose two Transformer based models, named, DeepSC-IR and DeepSC-MT, to perform image retrieval and machine translation, respectively. In this case, DeepSC-IR is trained to optimize the distance in embedding space between images and DeepSC-MT is trained to minimize the semantic errors by recovering the semantic meaning of sentences. For the multimodal multi-user system, we develop a Transformer enabled model, named, DeepSC-VQA, for the VQA task by extracting text-image information at the transmitters and fusing it at the receiver. In particular, a novel layer-wise Transformer is designed to help fuse multimodal data by adding connection between each of the encoder and decoder layers. Numerical results will show that the proposed models are superior to traditional communications in terms of the robustness to channels, computational complexity, transmission delay, and the task-execution performance at various task-specific metrics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.10255v1-abstract-full').style.display = 'none'; document.getElementById('2112.10255v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 11 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2110.08664">arXiv:2110.08664</a> <span> [<a href="https://arxiv.org/pdf/2110.08664">pdf</a>, <a href="https://arxiv.org/format/2110.08664">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Software Engineering">cs.SE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xinhai Zhang</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+J">Jianbo Tao</a>, <a href="/search/eess?searchtype=author&query=Tan%2C+K">Kaige Tan</a>, <a href="/search/eess?searchtype=author&query=T%C3%B6rngren%2C+M">Martin T枚rngren</a>, <a href="/search/eess?searchtype=author&query=S%C3%A1nchez%2C+J+M+G">Jos茅 Manuel Gaspar S谩nchez</a>, <a href="/search/eess?searchtype=author&query=Ramli%2C+M+R">Muhammad Rusyadi Ramli</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xin Tao</a>, <a href="/search/eess?searchtype=author&query=Gyllenhammar%2C+M">Magnus Gyllenhammar</a>, <a href="/search/eess?searchtype=author&query=Wotawa%2C+F">Franz Wotawa</a>, <a href="/search/eess?searchtype=author&query=Mohan%2C+N">Naveen Mohan</a>, <a href="/search/eess?searchtype=author&query=Nica%2C+M">Mihai Nica</a>, <a href="/search/eess?searchtype=author&query=Felbinger%2C+H">Hermann Felbinger</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2110.08664v1-abstract-short" style="display: inline;"> Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.08664v1-abstract-full').style.display = 'inline'; document.getElementById('2110.08664v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2110.08664v1-abstract-full" style="display: none;"> Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2110.08664v1-abstract-full').style.display = 'none'; document.getElementById('2110.08664v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 October, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">37 pages, 24 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.01871">arXiv:2106.01871</a> <span> [<a href="https://arxiv.org/pdf/2106.01871">pdf</a>, <a href="https://arxiv.org/format/2106.01871">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.ress.2021.108251">10.1016/j.ress.2021.108251 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Short-term Maintenance Planning of Autonomous Trucks for Minimizing Economic Risk </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xin Tao</a>, <a href="/search/eess?searchtype=author&query=M%C3%A5rtensson%2C+J">Jonas M氓rtensson</a>, <a href="/search/eess?searchtype=author&query=Warnquist%2C+H">H氓kan Warnquist</a>, <a href="/search/eess?searchtype=author&query=Pernest%C3%A5l%2C+A">Anna Pernest氓l</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.01871v1-abstract-short" style="display: inline;"> New autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator's profit. In this paper, we implement a vehicle health management system by addressing the maintenance plan… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.01871v1-abstract-full').style.display = 'inline'; document.getElementById('2106.01871v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.01871v1-abstract-full" style="display: none;"> New autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator's profit. In this paper, we implement a vehicle health management system by addressing the maintenance planning issues of autonomous trucks on a transport mission. We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company. Both availability losses and maintenance costs are considered when evaluating the economic risk. We demonstrate the proposed model by numerical experiments illustrating real-world scenarios. In the experiments, compared to three baseline methods, the expected economic risk of the proposed method is reduced by up to $47\%$. We also conduct sensitivity analyses of different model parameters. The analyses show that the economic risk significantly decreases when the estimation accuracy of remaining useful life, the maximal allowed time of delivery delay before order cancellation, or the number of workshops increases. The experiment results contribute to identifying future research and development attentions of autonomous trucks from an economic perspective. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.01871v1-abstract-full').style.display = 'none'; document.getElementById('2106.01871v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, 13 figures, journal, submitted to Reliability Engineering & System Safety, under review</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2103.11907">arXiv:2103.11907</a> <span> [<a href="https://arxiv.org/pdf/2103.11907">pdf</a>, <a href="https://arxiv.org/format/2103.11907">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> MEC-Empowered Non-Terrestrial Network for 6G Wide-Area Time-Sensitive Internet of Things </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+C">Chengxiao Liu</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+W">Wei Feng</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/eess?searchtype=author&query=Ge%2C+N">Ning Ge</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2103.11907v2-abstract-short" style="display: inline;"> In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) keeps increasing. As conventional cellular technologies are hard to be directly used for wide-area time-sensitive IoT, it is beneficial to use non-terrestrial infrastructures including satellites and unmanned aerial vehicles (UAVs), where a non-terrestrial network (NTN) can be… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.11907v2-abstract-full').style.display = 'inline'; document.getElementById('2103.11907v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2103.11907v2-abstract-full" style="display: none;"> In the upcoming sixth-generation (6G) era, the demand for constructing a wide-area time-sensitive Internet of Things (IoT) keeps increasing. As conventional cellular technologies are hard to be directly used for wide-area time-sensitive IoT, it is beneficial to use non-terrestrial infrastructures including satellites and unmanned aerial vehicles (UAVs), where a non-terrestrial network (NTN) can be built under the cell-free architecture. Driven by the time-sensitive requirements and uneven distribution of IoT devices, the NTN is required to be empowered by mobile edge computing (MEC) while providing oasis-oriented on-demand coverage for the devices. Nevertheless, communication and MEC systems are coupled with each other under the influence of complex propagation environment in the MEC-empowered NTN, which makes it hard to orchestrate the resources. In this paper, we propose a process-oriented framework to design the communication and MEC systems in a time-division manner. Under this framework, the large-scale channel state information (CSI) is used to characterize the complex propagation environment with an affordable cost, where a non-convex latency minimization problem is formulated. After that, the approximated problem is given and it can be decomposed into subproblems. These subproblems are further solved in an iterative way. Simulation results demonstrate the superiority of the proposed process-oriented scheme over other algorithms. These results also indicate that the payload deployments of UAVs should be appropriately predesigned to improve the efficiency of resource use. Furthermore, the results imply that it is advantageous to integrate NTN with MEC for wide-area time-sensitive IoT. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2103.11907v2-abstract-full').style.display = 'none'; document.getElementById('2103.11907v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 March, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">29 pages, 11 figures, one column</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" 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