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class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.16997">arXiv:2411.16997</a> <span> [<a href="https://arxiv.org/pdf/2411.16997">pdf</a>, <a href="https://arxiv.org/format/2411.16997">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 Modeling for Ultraviolet Non-Line-of-Sight Communications Incorporating an Obstacle </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+T">Tianfeng Wu</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+F">Fang Yang</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+T">Tian Cao</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+L">Ling Cheng</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yupeng Chen</a>, <a href="/search/eess?searchtype=author&query=Song%2C+J">Jian Song</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+J">Julian Cheng</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="2411.16997v1-abstract-short" style="display: inline;"> Existing studies on ultraviolet (UV) non-line-of-sight (NLoS) channel modeling primarily focus on scenarios without any obstacle, which makes them unsuitable for small transceiver elevation angles in most cases. To address this issue, a UV NLoS channel model incorporating an obstacle was investigated in this paper, where the impacts of atmospheric scattering and obstacle reflection on UV signals w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.16997v1-abstract-full').style.display = 'inline'; document.getElementById('2411.16997v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.16997v1-abstract-full" style="display: none;"> Existing studies on ultraviolet (UV) non-line-of-sight (NLoS) channel modeling primarily focus on scenarios without any obstacle, which makes them unsuitable for small transceiver elevation angles in most cases. To address this issue, a UV NLoS channel model incorporating an obstacle was investigated in this paper, where the impacts of atmospheric scattering and obstacle reflection on UV signals were both taken into account. To validate the proposed model, we compared it to the related Monte-Carlo photon-tracing (MCPT) model that had been verified by outdoor experiments. Numerical results manifest that the path loss curves obtained by the proposed model agree well with those determined by the MCPT model, while its computation complexity is lower than that of the MCPT model. This work discloses that obstacle reflection can effectively reduce the channel path loss of UV NLoS communication systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.16997v1-abstract-full').style.display = 'none'; document.getElementById('2411.16997v1-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, 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">Accepted by IEEE Global Communications Conference (GLOBECOM) 2024. arXiv admin note: substantial text overlap with arXiv:2411.15154</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.15154">arXiv:2411.15154</a> <span> [<a href="https://arxiv.org/pdf/2411.15154">pdf</a>, <a href="https://arxiv.org/format/2411.15154">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"> Modeling of UV NLoS Communication Channels: From Atmospheric Scattering and Obstacle Reflection Perspectives </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+T">Tianfeng Wu</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+F">Fang Yang</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+T">Tian Cao</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+L">Ling Cheng</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yupeng Chen</a>, <a href="/search/eess?searchtype=author&query=Song%2C+J">Jian Song</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+J">Julian Cheng</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="2411.15154v1-abstract-short" style="display: inline;"> As transceiver elevation angles increase from small to large, existing ultraviolet (UV) non-line-of-sight (NLoS) models encounter two challenges: i) cannot estimate the channel characteristics of UV NLoS communication scenarios when there exists an obstacle in the overlap volume between the transmitter beam and the receiver field-of-view (FoV), and ii) cannot evaluate the channel path loss for the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15154v1-abstract-full').style.display = 'inline'; document.getElementById('2411.15154v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.15154v1-abstract-full" style="display: none;"> As transceiver elevation angles increase from small to large, existing ultraviolet (UV) non-line-of-sight (NLoS) models encounter two challenges: i) cannot estimate the channel characteristics of UV NLoS communication scenarios when there exists an obstacle in the overlap volume between the transmitter beam and the receiver field-of-view (FoV), and ii) cannot evaluate the channel path loss for the wide beam and wide FoV scenarios with existing simplified single-scattering path loss models. To address these challenges, a UV NLoS scattering model incorporating an obstacle was investigated, where the obstacle's orientation angle, coordinates, and geometric dimensions were taken into account to approach actual application environments. Then, a UV NLoS reflection model was developed combined with specific geometric diagrams. Further, a simplified single-scattering path loss model was proposed with a closed-form expression. Finally, the proposed models were validated by comparing them with the Monte-Carlo photon-tracing model, the exact single-scattering model, and the latest simplified single-scattering model. Numerical results show that the path loss curves obtained by the proposed models agree well with those attained by related NLoS models under identical parameter settings, and avoiding obstacles is not always a good option for UV NLoS communications. Moreover, the accuracy of the proposed simplified model is superior to that of the existing simplified model for all kinds of transceiver FoV angles. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15154v1-abstract-full').style.display = 'none'; document.getElementById('2411.15154v1-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> 7 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">Accepted by IEEE Journal on Selected Areas in Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12985">arXiv:2411.12985</a> <span> [<a href="https://arxiv.org/pdf/2411.12985">pdf</a>, <a href="https://arxiv.org/format/2411.12985">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"> Disco Intelligent Omni-Surfaces: 360-degree Fully-Passive Jamming Attacks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Huang%2C+H">Huan Huang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+H">Hongliang Zhang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+J">Jide Yuan</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+L">Luyao Sun</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yitian Wang</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</a>, <a href="/search/eess?searchtype=author&query=Di%2C+B">Boya Di</a>, <a href="/search/eess?searchtype=author&query=Cai%2C+Y">Yi Cai</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="2411.12985v1-abstract-short" style="display: inline;"> Intelligent omni-surfaces (IOSs) with 360-degree electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a "DISCO" IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based F… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12985v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12985v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12985v1-abstract-full" style="display: none;"> Intelligent omni-surfaces (IOSs) with 360-degree electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a "DISCO" IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based FPJ, the interrelated refractive and reflective (R&R) coefficients of the adversarial IOS are randomly generated, acting like a "DISCO" that distributes wireless energy radiated by the base station. By introducing active channel aging (ACA) during channel coherence time, the DIOS-based FPJ can perform omnidirectional fully-passive jamming without neither jamming power nor channel knowledge of legitimate users (LUs). To characterize the impact of the DIOS-based PFJ, we derive the statistical characteristics of DIOS-jammed channels based on two widely-used IOS models, i.e., the constant-amplitude model and the variable-amplitude model. Consequently, the asymptotic analysis of the ergodic achievable sum rates under the DIOS-based omnidirectional fully-passive jamming is given based on the derived stochastic characteristics for both the two IOS models. Based on the derived analysis, the omnidirectional jamming impact of the proposed DIOS-based FPJ implemented by a constant-amplitude IOS does not depend on either the quantization number or the stochastic distribution of the DIOS coefficients, while the conclusion does not hold on when a variable-amplitude IOS is used. Numerical results based on one-bit quantization of the IOS phase shifts are provided to verify the effectiveness of the derived theoretical analysis. The proposed DIOS-based FPJ can not only launch omnidirectional fully-passive jamming, but also improve the jamming impact by about 55% at 10 dBm transmit power per LU. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12985v1-abstract-full').style.display = 'none'; document.getElementById('2411.12985v1-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 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">This paper has been submitted to IEEE TWC 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/2411.05363">arXiv:2411.05363</a> <span> [<a href="https://arxiv.org/pdf/2411.05363">pdf</a>, <a href="https://arxiv.org/format/2411.05363">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"> Single-Collision Model for Non-Line-of-Sight UV Communication Channel With Obstacle </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+T">Tianfeng Wu</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+F">Fang Yang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+R">Renzhi Yuan</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+T">Tian Cao</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+L">Ling Cheng</a>, <a href="/search/eess?searchtype=author&query=Song%2C+J">Jian Song</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+J">Julian Cheng</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="2411.05363v1-abstract-short" style="display: inline;"> Existing research on non-line-of-sight (NLoS) ultraviolet (UV) channel modeling mainly focuses on scenarios where the signal propagation process is not affected by any obstacle and the radiation intensity (RI) of the light source is uniformly distributed. To eliminate these restrictions, we propose a single-collision model for the NLoS UV channel incorporating a cuboid-shaped obstacle, where the R… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05363v1-abstract-full').style.display = 'inline'; document.getElementById('2411.05363v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.05363v1-abstract-full" style="display: none;"> Existing research on non-line-of-sight (NLoS) ultraviolet (UV) channel modeling mainly focuses on scenarios where the signal propagation process is not affected by any obstacle and the radiation intensity (RI) of the light source is uniformly distributed. To eliminate these restrictions, we propose a single-collision model for the NLoS UV channel incorporating a cuboid-shaped obstacle, where the RI of the UV light source is modeled as the Lambertian distribution. For easy interpretation, we categorize the intersection circumstances between the receiver field-of-view and the obstacle into six cases and provide derivations of the weighting factor for each case. To investigate the accuracy of the proposed model, we compare it with the associated Monte Carlo photon tracing model via simulations and experiments. Results verify the correctness of the proposed model. This work reveals that obstacle avoidance is not always beneficial for NLoS UV communications and provides guidelines for relevant system design. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.05363v1-abstract-full').style.display = 'none'; document.getElementById('2411.05363v1-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, 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">Submitted to IEEE International Conference on Communications (ICC) 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.01194">arXiv:2411.01194</a> <span> [<a href="https://arxiv.org/pdf/2411.01194">pdf</a>, <a href="https://arxiv.org/format/2411.01194">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"> Relay Satellite Assisted LEO Constellation NOMA Communication System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xuyang Zhang</a>, <a href="/search/eess?searchtype=author&query=Yue%2C+X">Xinwei Yue</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhihao Han</a>, <a href="/search/eess?searchtype=author&query=Li%2C+T">Tian Li</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+X">Xia Shen</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yafei Wang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+R">Rongke Liu</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="2411.01194v1-abstract-short" style="display: inline;"> This paper proposes a relay satellite assisted low earth orbit (LEO) constellation non-orthogonal multiple access combined beamforming (R-NOMA-BF) communication system, where multiple antenna LEO satellites deliver information to ground non-orthogonal users. To measure the service quality, we formulate a resource allocation problem to minimize the second-order difference between the achievable cap… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01194v1-abstract-full').style.display = 'inline'; document.getElementById('2411.01194v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.01194v1-abstract-full" style="display: none;"> This paper proposes a relay satellite assisted low earth orbit (LEO) constellation non-orthogonal multiple access combined beamforming (R-NOMA-BF) communication system, where multiple antenna LEO satellites deliver information to ground non-orthogonal users. To measure the service quality, we formulate a resource allocation problem to minimize the second-order difference between the achievable capacity and user request traffic. Based on the above problem, joint optimization for LEO satellite-cell assignment factor, NOMA power and BF vector is taken into account. The optimization variables are analyzed with respect to feasibility and non-convexity. Additionally, we provide a pair of effective algorithms, i.e., doppler shift LEO satellite-cell assisted monotonic programming of NOMA with BF vector (D-mNOMA-BF) and ant colony pathfinding based NOMA exponential cone programming with BF vector (A-eNOMA-BF). Two compromise algorithms regarding the above are also presented. Numerical results show that: 1) D-mNOMA-BF and A-eNOMA-BF algorithms are superior to that of orthogonal multiple access based BF (OMA-BF) and polarization multiplexing schemes; 2) With the increasing number of antennas and single satellite power, R-NOMA-BF system is able to expand users satisfaction; and 3) By comparing various imperfect successive interference cancellation, the performance of A-mNOMA-BF algorithm exceeds D-mNOMA-BF. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01194v1-abstract-full').style.display = 'none'; document.getElementById('2411.01194v1-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> 2 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20344">arXiv:2410.20344</a> <span> [<a href="https://arxiv.org/pdf/2410.20344">pdf</a>, <a href="https://arxiv.org/format/2410.20344">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"> Deep Learning-Assisted Jamming Mitigation with Movable Antenna Array </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tang%2C+X">Xiao Tang</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+Y">Yudan Jiang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+J">Jinxin Liu</a>, <a href="/search/eess?searchtype=author&query=Du%2C+Q">Qinghe Du</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</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="2410.20344v1-abstract-short" style="display: inline;"> This paper reveals the potential of movable antennas in enhancing anti-jamming communication. We consider a legitimate communication link in the presence of multiple jammers and propose deploying a movable antenna array at the receiver to combat jamming attacks. We formulate the problem as a signal-to-interference-plus-noise ratio maximization, by jointly optimizing the receive beamforming and ant… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20344v1-abstract-full').style.display = 'inline'; document.getElementById('2410.20344v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20344v1-abstract-full" style="display: none;"> This paper reveals the potential of movable antennas in enhancing anti-jamming communication. We consider a legitimate communication link in the presence of multiple jammers and propose deploying a movable antenna array at the receiver to combat jamming attacks. We formulate the problem as a signal-to-interference-plus-noise ratio maximization, by jointly optimizing the receive beamforming and antenna element positioning. Due to the non-convexity and multi-fold difficulties from an optimization perspective, we develop a deep learning-based framework where beamforming is tackled as a Rayleigh quotient problem, while antenna positioning is addressed through multi-layer perceptron training. The neural network parameters are optimized using stochastic gradient descent to achieve effective jamming mitigation strategy, featuring offline training with marginal complexity for online inference. Numerical results demonstrate that the proposed approach achieves near-optimal anti-jamming performance thereby significantly improving the efficiency in strategy determination. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20344v1-abstract-full').style.display = 'none'; document.getElementById('2410.20344v1-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 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">5 pages, under consideration</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.14996">arXiv:2410.14996</a> <span> [<a href="https://arxiv.org/pdf/2410.14996">pdf</a>, <a href="https://arxiv.org/format/2410.14996">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"> EDRF: Enhanced Driving Risk Field Based on Multimodal Trajectory Prediction and Its Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Jiang%2C+J">Junkai Jiang</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zeyu Han</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yuning Wang</a>, <a href="/search/eess?searchtype=author&query=Cai%2C+M">Mengchi Cai</a>, <a href="/search/eess?searchtype=author&query=Meng%2C+Q">Qingwen Meng</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Q">Qing Xu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Jianqiang 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="2410.14996v1-abstract-short" style="display: inline;"> Driving risk assessment is crucial for both autonomous vehicles and human-driven vehicles. The driving risk can be quantified as the product of the probability that an event (such as collision) will occur and the consequence of that event. However, the probability of events occurring is often difficult to predict due to the uncertainty of drivers' or vehicles' behavior. Traditional methods general… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14996v1-abstract-full').style.display = 'inline'; document.getElementById('2410.14996v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.14996v1-abstract-full" style="display: none;"> Driving risk assessment is crucial for both autonomous vehicles and human-driven vehicles. The driving risk can be quantified as the product of the probability that an event (such as collision) will occur and the consequence of that event. However, the probability of events occurring is often difficult to predict due to the uncertainty of drivers' or vehicles' behavior. Traditional methods generally employ kinematic-based approaches to predict the future trajectories of entities, which often yield unrealistic prediction results. In this paper, the Enhanced Driving Risk Field (EDRF) model is proposed, integrating deep learning-based multimodal trajectory prediction results with Gaussian distribution models to quantitatively capture the uncertainty of traffic entities' behavior. The applications of the EDRF are also proposed. It is applied across various tasks (traffic risk monitoring, ego-vehicle risk analysis, and motion and trajectory planning) through the defined concept Interaction Risk (IR). Adequate example scenarios are provided for each application to illustrate the effectiveness of the model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14996v1-abstract-full').style.display = 'none'; document.getElementById('2410.14996v1-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 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/2410.02796">arXiv:2410.02796</a> <span> [<a href="https://arxiv.org/pdf/2410.02796">pdf</a>, <a href="https://arxiv.org/format/2410.02796">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="Emerging Technologies">cs.ET</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Toward Adaptive Tracking and Communication via an Airborne Maneuverable Bi-Static ISAC System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wei%2C+M">Mingliang Wei</a>, <a href="/search/eess?searchtype=author&query=Li%2C+R">Ruoguang Li</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+L">Li Wang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+L">Lianming Xu</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="2410.02796v1-abstract-short" style="display: inline;"> In this letter, we propose an airborne maneuverable bi-static integrated sensing and communication system where both the transmitter and receiver are unmanned aerial vehicles. By timely forming a dynamic bi-static range based on the motion information of the target, such a system can provide an adaptive two dimensional tracking and communication services. Towards this end, a trajectory optimizatio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02796v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02796v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02796v1-abstract-full" style="display: none;"> In this letter, we propose an airborne maneuverable bi-static integrated sensing and communication system where both the transmitter and receiver are unmanned aerial vehicles. By timely forming a dynamic bi-static range based on the motion information of the target, such a system can provide an adaptive two dimensional tracking and communication services. Towards this end, a trajectory optimization problem for both transmits and receive UAV is formulated to achieve high-accurate motion state estimation by minimizing the time-variant Cramer Rao bound, subject to the sufficient communication signal-to-noise ratio to maintain communication channel prediction error. Then we develop an efficient approach based on the successive convex approximation technique and the S-procedure to address the problem. Numerical results demonstrate that our proposed airborne maneuverable bi-static ISAC system is able to obtain higher tracking accuracy compared with the static or semi-dynamic ISAC system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02796v1-abstract-full').style.display = 'none'; document.getElementById('2410.02796v1-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 September, 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/2410.02122">arXiv:2410.02122</a> <span> [<a href="https://arxiv.org/pdf/2410.02122">pdf</a>, <a href="https://arxiv.org/ps/2410.02122">ps</a>, <a href="https://arxiv.org/format/2410.02122">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="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Resource Allocation Based on Optimal Transport Theory in ISAC-Enabled Multi-UAV Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zheng%2C+Y">Yufeng Zheng</a>, <a href="/search/eess?searchtype=author&query=Li%2C+L">Lixin Li</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+W">Wensheng Lin</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+W">Wei Liang</a>, <a href="/search/eess?searchtype=author&query=Du%2C+Q">Qinghe Du</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="2410.02122v1-abstract-short" style="display: inline;"> This paper investigates the resource allocation optimization for cooperative communication with non-cooperative localization in integrated sensing and communications (ISAC)-enabled multi-unmanned aerial vehicle (UAV) cooperative networks. Our goal is to maximize the weighted sum of the system's average sum rate and the localization quality of service (QoS) by jointly optimizing cell association, c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02122v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02122v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02122v1-abstract-full" style="display: none;"> This paper investigates the resource allocation optimization for cooperative communication with non-cooperative localization in integrated sensing and communications (ISAC)-enabled multi-unmanned aerial vehicle (UAV) cooperative networks. Our goal is to maximize the weighted sum of the system's average sum rate and the localization quality of service (QoS) by jointly optimizing cell association, communication power allocation, and sensing power allocation. Since the formulated problem is a mixed-integer nonconvex problem, we propose the alternating iteration algorithm based on optimal transport theory (AIBOT) to solve the optimization problem more effectively. Simulation results demonstrate that the AIBOT can improve the system sum rate by nearly 12% and reduce the localization Cr'amer-Rao bound (CRB) by almost 29% compared to benchmark algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02122v1-abstract-full').style.display = 'none'; document.getElementById('2410.02122v1-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> 2 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/2410.02121">arXiv:2410.02121</a> <span> [<a href="https://arxiv.org/pdf/2410.02121">pdf</a>, <a href="https://arxiv.org/format/2410.02121">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> SC-CDM: Enhancing Quality of Image Semantic Communication with a Compact Diffusion Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+K">Kexin Zhang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+L">Lixin Li</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+W">Wensheng Lin</a>, <a href="/search/eess?searchtype=author&query=Yan%2C+Y">Yuna Yan</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+W">Wenchi Cheng</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="2410.02121v1-abstract-short" style="display: inline;"> Semantic Communication (SC) is an emerging technology that has attracted much attention in the sixth-generation (6G) mobile communication systems. However, few literature has fully considered the perceptual quality of the reconstructed image. To solve this problem, we propose a generative SC for wireless image transmission (denoted as SC-CDM). This approach leverages compact diffusion models to im… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02121v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02121v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02121v1-abstract-full" style="display: none;"> Semantic Communication (SC) is an emerging technology that has attracted much attention in the sixth-generation (6G) mobile communication systems. However, few literature has fully considered the perceptual quality of the reconstructed image. To solve this problem, we propose a generative SC for wireless image transmission (denoted as SC-CDM). This approach leverages compact diffusion models to improve the fidelity and semantic accuracy of the images reconstructed after transmission, ensuring that the essential content is preserved even in bandwidth-constrained environments. Specifically, we aim to redesign the swin Transformer as a new backbone for efficient semantic feature extraction and compression. Next, the receiver integrates the slim prior and image reconstruction networks. Compared to traditional Diffusion Models (DMs), it leverages DMs' robust distribution mapping capability to generate a compact condition vector, guiding image recovery, thus enhancing the perceptual details of the reconstructed images. Finally, a series of evaluation and ablation studies are conducted to validate the effectiveness and robustness of the proposed algorithm and further increase the Peak Signal-to-Noise Ratio (PSNR) by over 17% on top of CNN-based DeepJSCC. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02121v1-abstract-full').style.display = 'none'; document.getElementById('2410.02121v1-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> 2 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">arXiv admin note: text overlap with arXiv:2408.05112</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.02120">arXiv:2410.02120</a> <span> [<a href="https://arxiv.org/pdf/2410.02120">pdf</a>, <a href="https://arxiv.org/ps/2410.02120">ps</a>, <a href="https://arxiv.org/format/2410.02120">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="Machine Learning">cs.LG</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"> Lossy Cooperative UAV Relaying Networks: Outage Probability Analysis and Location Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lian%2C+Y">Ya Lian</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+W">Wensheng Lin</a>, <a href="/search/eess?searchtype=author&query=Li%2C+L">Lixin Li</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+F">Fucheng Yang</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Matsumoto%2C+T">Tad Matsumoto</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="2410.02120v1-abstract-short" style="display: inline;"> In this paper, performance of a lossy cooperative unmanned aerial vehicle (UAV) relay communication system is analyzed. In this system, the UAV relay adopts lossy forward (LF) strategy and the receiver has certain distortion requirements for the received information. For the system described above, we first derive the achievable rate distortion region of the system. Then, on the basis of the regio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02120v1-abstract-full').style.display = 'inline'; document.getElementById('2410.02120v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.02120v1-abstract-full" style="display: none;"> In this paper, performance of a lossy cooperative unmanned aerial vehicle (UAV) relay communication system is analyzed. In this system, the UAV relay adopts lossy forward (LF) strategy and the receiver has certain distortion requirements for the received information. For the system described above, we first derive the achievable rate distortion region of the system. Then, on the basis of the region analysis, the system outage probability when the channel suffers Nakagami-$m$ fading is analyzed. Finally, we design an optimal relay position identification algorithm based on the Soft Actor-Critic (SAC) algorithm, which determines the optimal UAV position to minimize the outage probability. The simulation results show that the proposed algorithm can optimize the UAV position and reduce the system outage probability effectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.02120v1-abstract-full').style.display = 'none'; document.getElementById('2410.02120v1-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> 2 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/2410.01597">arXiv:2410.01597</a> <span> [<a href="https://arxiv.org/pdf/2410.01597">pdf</a>, <a href="https://arxiv.org/format/2410.01597">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="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"> SAFE: Semantic Adaptive Feature Extraction with Rate Control for 6G Wireless Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yan%2C+Y">Yuna Yan</a>, <a href="/search/eess?searchtype=author&query=Li%2C+L">Lixin Li</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xin Zhang</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+W">Wensheng Lin</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+W">Wenchi Cheng</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="2410.01597v1-abstract-short" style="display: inline;"> Most current Deep Learning-based Semantic Communication (DeepSC) systems are designed and trained exclusively for particular single-channel conditions, which restricts their adaptability and overall bandwidth utilization. To address this, we propose an innovative Semantic Adaptive Feature Extraction (SAFE) framework, which significantly improves bandwidth efficiency by allowing users to select dif… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01597v1-abstract-full').style.display = 'inline'; document.getElementById('2410.01597v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.01597v1-abstract-full" style="display: none;"> Most current Deep Learning-based Semantic Communication (DeepSC) systems are designed and trained exclusively for particular single-channel conditions, which restricts their adaptability and overall bandwidth utilization. To address this, we propose an innovative Semantic Adaptive Feature Extraction (SAFE) framework, which significantly improves bandwidth efficiency by allowing users to select different sub-semantic combinations based on their channel conditions. This paper also introduces three advanced learning algorithms to optimize the performance of SAFE framework as a whole. Through a series of simulation experiments, we demonstrate that the SAFE framework can effectively and adaptively extract and transmit semantics under different channel bandwidth conditions, of which effectiveness is verified through objective and subjective quality evaluations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01597v1-abstract-full').style.display = 'none'; document.getElementById('2410.01597v1-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> 2 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/2409.16920">arXiv:2409.16920</a> <span> [<a href="https://arxiv.org/pdf/2409.16920">pdf</a>, <a href="https://arxiv.org/format/2409.16920">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="Artificial Intelligence">cs.AI</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="Human-Computer Interaction">cs.HC</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"> Cross-lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhichen Han</a>, <a href="/search/eess?searchtype=author&query=Geng%2C+T">Tianqi Geng</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+H">Hui Feng</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+J">Jiahong Yuan</a>, <a href="/search/eess?searchtype=author&query=Richmond%2C+K">Korin Richmond</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yuanchao 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="2409.16920v1-abstract-short" style="display: inline;"> Utilizing Self-Supervised Learning (SSL) models for Speech Emotion Recognition (SER) has proven effective, yet limited research has explored cross-lingual scenarios. This study presents a comparative analysis between human performance and SSL models, beginning with a layer-wise analysis and an exploration of parameter-efficient fine-tuning strategies in monolingual, cross-lingual, and transfer lea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16920v1-abstract-full').style.display = 'inline'; document.getElementById('2409.16920v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.16920v1-abstract-full" style="display: none;"> Utilizing Self-Supervised Learning (SSL) models for Speech Emotion Recognition (SER) has proven effective, yet limited research has explored cross-lingual scenarios. This study presents a comparative analysis between human performance and SSL models, beginning with a layer-wise analysis and an exploration of parameter-efficient fine-tuning strategies in monolingual, cross-lingual, and transfer learning contexts. We further compare the SER ability of models and humans at both utterance- and segment-levels. Additionally, we investigate the impact of dialect on cross-lingual SER through human evaluation. Our findings reveal that models, with appropriate knowledge transfer, can adapt to the target language and achieve performance comparable to native speakers. We also demonstrate the significant effect of dialect on SER for individuals without prior linguistic and paralinguistic background. Moreover, both humans and models exhibit distinct behaviors across different emotions. These results offer new insights into the cross-lingual SER capabilities of SSL models, underscoring both their similarities to and differences from human emotion perception. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16920v1-abstract-full').style.display = 'none'; document.getElementById('2409.16920v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.14726">arXiv:2409.14726</a> <span> [<a href="https://arxiv.org/pdf/2409.14726">pdf</a>, <a href="https://arxiv.org/format/2409.14726">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</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 Communication Enabled 6G-NTN Framework: A Novel Denoising and Gateway Hop Integration Mechanism </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Nguyen%2C+L+X">Loc X. Nguyen</a>, <a href="/search/eess?searchtype=author&query=Hassan%2C+S+S">Sheikh Salman Hassan</a>, <a href="/search/eess?searchtype=author&query=Tun%2C+Y+K">Yan Kyaw Tun</a>, <a href="/search/eess?searchtype=author&query=Kim%2C+K">Kitae Kim</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Hong%2C+C+S">Choong Seon Hong</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="2409.14726v1-abstract-short" style="display: inline;"> The sixth-generation (6G) non-terrestrial networks (NTNs) are crucial for real-time monitoring in critical applications like disaster relief. However, limited bandwidth, latency, rain attenuation, long propagation delays, and co-channel interference pose challenges to efficient satellite communication. Therefore, semantic communication (SC) has emerged as a promising solution to improve transmissi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14726v1-abstract-full').style.display = 'inline'; document.getElementById('2409.14726v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.14726v1-abstract-full" style="display: none;"> The sixth-generation (6G) non-terrestrial networks (NTNs) are crucial for real-time monitoring in critical applications like disaster relief. However, limited bandwidth, latency, rain attenuation, long propagation delays, and co-channel interference pose challenges to efficient satellite communication. Therefore, semantic communication (SC) has emerged as a promising solution to improve transmission efficiency and address these issues. In this paper, we explore the potential of SC as a bandwidth-efficient, latency-minimizing strategy specifically suited to 6G satellite communications. While existing SC methods have demonstrated efficacy in direct satellite-terrestrial transmissions, they encounter limitations in satellite networks due to distortion accumulation across gateway hop-relays. Additionally, certain ground users (GUs) experience poor signal-to-noise ratios (SNR), making direct satellite communication challenging. To address these issues, we propose a novel framework that optimizes gateway hop-relay selection for GUs with low SNR and integrates gateway-based denoising mechanisms to ensure high-quality-of-service (QoS) in satellite-based SC networks. This approach directly mitigates distortion, leading to significant improvements in satellite service performance by delivering customized services tailored to the unique signal conditions of each GU. Our findings represent a critical advancement in reliable and efficient data transmission from the Earth observation satellites, thereby enabling fast and effective responses to urgent events. Simulation results demonstrate that our proposed strategy significantly enhances overall network performance, outperforming conventional methods by offering tailored communication services based on specific GU conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14726v1-abstract-full').style.display = 'none'; document.getElementById('2409.14726v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">13 pages, 8 figures, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.14688">arXiv:2409.14688</a> <span> [<a href="https://arxiv.org/pdf/2409.14688">pdf</a>, <a href="https://arxiv.org/format/2409.14688">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</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"> A Generalized Control Revision Method for Autonomous Driving Safety </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+Z">Zehang Zhu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yuning Wang</a>, <a href="/search/eess?searchtype=author&query=Ke%2C+T">Tianqi Ke</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zeyu Han</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+S">Shaobing Xu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Q">Qing Xu</a>, <a href="/search/eess?searchtype=author&query=Dolan%2C+J+M">John M. Dolan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Jianqiang 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="2409.14688v1-abstract-short" style="display: inline;"> Safety is one of the most crucial challenges of autonomous driving vehicles, and one solution to guarantee safety is to employ an additional control revision module after the planning backbone. Control Barrier Function (CBF) has been widely used because of its strong mathematical foundation on safety. However, the incompatibility with heterogeneous perception data and incomplete consideration of t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14688v1-abstract-full').style.display = 'inline'; document.getElementById('2409.14688v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.14688v1-abstract-full" style="display: none;"> Safety is one of the most crucial challenges of autonomous driving vehicles, and one solution to guarantee safety is to employ an additional control revision module after the planning backbone. Control Barrier Function (CBF) has been widely used because of its strong mathematical foundation on safety. However, the incompatibility with heterogeneous perception data and incomplete consideration of traffic scene elements make existing systems hard to be applied in dynamic and complex real-world scenarios. In this study, we introduce a generalized control revision method for autonomous driving safety, which adopts both vectorized perception and occupancy grid map as inputs and comprehensively models multiple types of traffic scene constraints based on a new proposed barrier function. Traffic elements are integrated into one unified framework, decoupled from specific scenario settings or rules. Experiments on CARLA, SUMO, and OnSite simulator prove that the proposed algorithm could realize safe control revision under complicated scenes, adapting to various planning backbones, road topologies, and risk types. Physical platform validation also verifies the real-world application feasibility. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14688v1-abstract-full').style.display = 'none'; document.getElementById('2409.14688v1-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> 22 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.00501">arXiv:2409.00501</a> <span> [<a href="https://arxiv.org/pdf/2409.00501">pdf</a>, <a href="https://arxiv.org/ps/2409.00501">ps</a>, <a href="https://arxiv.org/format/2409.00501">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> <p class="title is-5 mathjax"> Leaky Wave Antenna-Equipped RF Chipless Tags for Orientation Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=L%C3%B3pez%2C+O+L+A">Onel L. A. L贸pez</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Sabharwal%2C+A">Ashutosh Sabharwal</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="2409.00501v1-abstract-short" style="display: inline;"> Accurate orientation estimation of an object in a scene is critical in robotics, aerospace, augmented reality, and medicine, as it supports scene understanding. This paper introduces a novel orientation estimation approach leveraging radio frequency (RF) sensing technology and leaky-wave antennas (LWAs). Specifically, we propose a framework for a radar system to estimate the orientation of a \text… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00501v1-abstract-full').style.display = 'inline'; document.getElementById('2409.00501v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.00501v1-abstract-full" style="display: none;"> Accurate orientation estimation of an object in a scene is critical in robotics, aerospace, augmented reality, and medicine, as it supports scene understanding. This paper introduces a novel orientation estimation approach leveraging radio frequency (RF) sensing technology and leaky-wave antennas (LWAs). Specifically, we propose a framework for a radar system to estimate the orientation of a \textit{dumb} LWA-equipped backscattering tag, marking the first exploration of this method in the literature. Our contributions include a comprehensive framework for signal modeling and orientation estimation with multi-subcarrier transmissions, and the formulation of a maximum likelihood estimator (MLE). Moreover, we analyze the impact of imperfect tag location information, revealing that it minimally affects estimation accuracy. Exploiting related results, we propose an approximate MLE and introduce a low-complexity radiation-pointing angle-based estimator with near-optimal performance. We derive the feasible orientation estimation region of the latter and show that it depends mainly on the system bandwidth. Our analytical results are validated through Monte Carlo simulations and reveal that the low-complexity estimator achieves near-optimal accuracy and that its feasible orientation estimation region is also approximately shared by the other estimators. Finally, we show that the optimal number of subcarriers increases with sensing time under a power budget constraint. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00501v1-abstract-full').style.display = 'none'; document.getElementById('2409.00501v1-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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, 2 tables, 8 figs. Submitted to IEEE TWC</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 93E11; 94A05; 68T10 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> C.2.1; B.4.7; I.5.4 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.12860">arXiv:2408.12860</a> <span> [<a href="https://arxiv.org/pdf/2408.12860">pdf</a>, <a href="https://arxiv.org/format/2408.12860">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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Active STAR-RIS Empowered Edge System for Enhanced Energy Efficiency and Task Management </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Aung%2C+P+S">Pyae Sone Aung</a>, <a href="/search/eess?searchtype=author&query=Kim%2C+K">Kitae Kim</a>, <a href="/search/eess?searchtype=author&query=Tun%2C+Y+K">Yan Kyaw Tun</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Hong%2C+C+S">Choong Seon Hong</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="2408.12860v1-abstract-short" style="display: inline;"> The proliferation of data-intensive and low-latency applications has driven the development of multi-access edge computing (MEC) as a viable solution to meet the increasing demands for high-performance computing and storage capabilities at the network edge. Despite the benefits of MEC, challenges such as obstructions cause non-line-of-sight (NLoS) communication to persist. Reconfigurable intellige… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.12860v1-abstract-full').style.display = 'inline'; document.getElementById('2408.12860v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.12860v1-abstract-full" style="display: none;"> The proliferation of data-intensive and low-latency applications has driven the development of multi-access edge computing (MEC) as a viable solution to meet the increasing demands for high-performance computing and storage capabilities at the network edge. Despite the benefits of MEC, challenges such as obstructions cause non-line-of-sight (NLoS) communication to persist. Reconfigurable intelligent surfaces (RISs) and the more advanced simultaneously transmitting and reflecting (STAR)-RISs have emerged to address these challenges; however, practical limitations and multiplicative fading effects hinder their efficacy. We propose an active STAR-RIS-assisted MEC system to overcome these obstacles, leveraging the advantages of active STAR-RIS. The main contributions consist of formulating an optimization problem to minimize energy consumption with task queue stability by jointly optimizing the partial task offloading, amplitude, phase shift coefficients, amplification coefficients, transmit power of the base station (BS), and admitted tasks. Furthermore, we decompose the non-convex problem into manageable sub-problems, employing sequential fractional programming for transmit power control, convex optimization technique for task offloading, and Lyapunov optimization with double deep Q-network (DDQN) for joint amplitude, phase shift, amplification, and task admission. Extensive performance evaluations demonstrate the superiority of the proposed system over benchmark schemes, highlighting its potential for enhancing MEC system performance. Numerical results indicate that our proposed system outperforms the conventional STAR-RIS-assisted by 18.64\% and the conventional RIS-assisted system by 30.43\%, respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.12860v1-abstract-full').style.display = 'none'; document.getElementById('2408.12860v1-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 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">13 pages, 10 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/2408.05112">arXiv:2408.05112</a> <span> [<a href="https://arxiv.org/pdf/2408.05112">pdf</a>, <a href="https://arxiv.org/format/2408.05112">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"> Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+K">Kexin Zhang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+L">Lixin Li</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+W">Wensheng Lin</a>, <a href="/search/eess?searchtype=author&query=Yan%2C+Y">Yuna Yan</a>, <a href="/search/eess?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+W">Wenchi Cheng</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="2408.05112v1-abstract-short" style="display: inline;"> Semantic Communication (SC) is an emerging technology aiming to surpass the Shannon limit. Traditional SC strategies often minimize signal distortion between the original and reconstructed data, neglecting perceptual quality, especially in low Signal-to-Noise Ratio (SNR) environments. To address this issue, we introduce a novel Generative AI Semantic Communication (GSC) system for single-user scen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05112v1-abstract-full').style.display = 'inline'; document.getElementById('2408.05112v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.05112v1-abstract-full" style="display: none;"> Semantic Communication (SC) is an emerging technology aiming to surpass the Shannon limit. Traditional SC strategies often minimize signal distortion between the original and reconstructed data, neglecting perceptual quality, especially in low Signal-to-Noise Ratio (SNR) environments. To address this issue, we introduce a novel Generative AI Semantic Communication (GSC) system for single-user scenarios. This system leverages deep generative models to establish a new paradigm in SC. Specifically, At the transmitter end, it employs a joint source-channel coding mechanism based on the Swin Transformer for efficient semantic feature extraction and compression. At the receiver end, an advanced Diffusion Model (DM) reconstructs high-quality images from degraded signals, enhancing perceptual details. Additionally, we present a Multi-User Generative Semantic Communication (MU-GSC) system utilizing an asynchronous processing model. This model effectively manages multiple user requests and optimally utilizes system resources for parallel processing. Simulation results on public datasets demonstrate that our generative AI semantic communication systems achieve superior transmission efficiency and enhanced communication content quality across various channel conditions. Compared to CNN-based DeepJSCC, our methods improve the Peak Signal-to-Noise Ratio (PSNR) by 17.75% in Additive White Gaussian Noise (AWGN) channels and by 20.86% in Rayleigh channels. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05112v1-abstract-full').style.display = 'none'; document.getElementById('2408.05112v1-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 July, 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/2408.04927">arXiv:2408.04927</a> <span> [<a href="https://arxiv.org/pdf/2408.04927">pdf</a>, <a href="https://arxiv.org/format/2408.04927">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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Large Models for Aerial Edges: An Edge-Cloud Model Evolution and Communication Paradigm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Shuhang Zhang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Q">Qingyu Liu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+K">Ke Chen</a>, <a href="/search/eess?searchtype=author&query=Di%2C+B">Boya Di</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+H">Hongliang Zhang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+W">Wenhan Yang</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Poor%2C+H+V">H. Vincent Poor</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="2408.04927v1-abstract-short" style="display: inline;"> The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground facility deployments in both communication and computing domains. In this network, aerial facilities, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04927v1-abstract-full').style.display = 'inline'; document.getElementById('2408.04927v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.04927v1-abstract-full" style="display: none;"> The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground facility deployments in both communication and computing domains. In this network, aerial facilities, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated edge-cloud model evolution framework, where UAVs serve as edge nodes for data collection and edge model computation. Through wireless channels, UAVs collaborate with ground cloud servers, providing cloud model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communication bandwidths and data sizes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04927v1-abstract-full').style.display = 'none'; document.getElementById('2408.04927v1-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 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/2408.03959">arXiv:2408.03959</a> <span> [<a href="https://arxiv.org/pdf/2408.03959">pdf</a>, <a href="https://arxiv.org/format/2408.03959">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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Semantic Enabled 6G LEO Satellite Communication for Earth Observation: A Resource-Constrained Network Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hassan%2C+S+S">Sheikh Salman Hassan</a>, <a href="/search/eess?searchtype=author&query=Nguyen%2C+L+X">Loc X. Nguyen</a>, <a href="/search/eess?searchtype=author&query=Tun%2C+Y+K">Yan Kyaw Tun</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Hong%2C+C+S">Choong Seon Hong</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="2408.03959v1-abstract-short" style="display: inline;"> Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to traditional communication methods. The rationality f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03959v1-abstract-full').style.display = 'inline'; document.getElementById('2408.03959v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.03959v1-abstract-full" style="display: none;"> Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to traditional communication methods. The rationality for adopting SC is its inherent ability to reduce communication costs and make spectrum efficient for 6G non-terrestrial networks (6G-NTNs). We focus on the critical satellite imagery downlink communications latency optimization for Earth observation through SC techniques. We formulate the latency minimization problem with SC quality-of-service (SC-QoS) constraints and address this problem with a meta-heuristic discrete whale optimization algorithm (DWOA) and a one-to-one matching game. The proposed approach for captured image processing and transmission includes the integration of joint semantic and channel encoding to ensure downlink sum-rate optimization and latency minimization. Empirical results from experiments demonstrate the efficiency of the proposed framework for latency optimization while preserving high-quality data transmission when compared to baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.03959v1-abstract-full').style.display = 'none'; document.getElementById('2408.03959v1-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 July, 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">Accepted in GLOBECOM 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/2408.02713">arXiv:2408.02713</a> <span> [<a href="https://arxiv.org/pdf/2408.02713">pdf</a>, <a href="https://arxiv.org/format/2408.02713">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</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="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</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/10.1080/24699322.2024.2357164">10.1080/24699322.2024.2357164 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Review on Organ Deformation Modeling Approaches for Reliable Surgical Navigation using Augmented Reality </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zheng Han</a>, <a href="/search/eess?searchtype=author&query=Dou%2C+Q">Qi Dou</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="2408.02713v1-abstract-short" style="display: inline;"> Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02713v1-abstract-full').style.display = 'inline'; document.getElementById('2408.02713v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.02713v1-abstract-full" style="display: none;"> Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02713v1-abstract-full').style.display = 'none'; document.getElementById('2408.02713v1-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 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/2408.02549">arXiv:2408.02549</a> <span> [<a href="https://arxiv.org/pdf/2408.02549">pdf</a>, <a href="https://arxiv.org/format/2408.02549">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"> Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhou%2C+H">Hao Zhou</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+C">Chengming Hu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+D">Dun Yuan</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+Y">Ye Yuan</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+D">Di Wu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+X">Xue Liu</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+C">Charlie 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="2408.02549v1-abstract-short" style="display: inline;"> Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large language models (LLMs) have attracted considerable interest from academia and telecom industry. This work considers a novel edge-cloud deployment of foundation models in 6G networks. Specifically, it aims to minimize the service delay of foundation models by radio r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02549v1-abstract-full').style.display = 'inline'; document.getElementById('2408.02549v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.02549v1-abstract-full" style="display: none;"> Generative artificial intelligence (GAI) is a promising technique towards 6G networks, and generative foundation models such as large language models (LLMs) have attracted considerable interest from academia and telecom industry. This work considers a novel edge-cloud deployment of foundation models in 6G networks. Specifically, it aims to minimize the service delay of foundation models by radio resource allocation and task offloading, i.e., offloading diverse content generation tasks to proper LLMs at the network edge or cloud. In particular, we first introduce the communication system model, i.e., allocating radio resources and calculating link capacity to support generated content transmission, and then we present the LLM inference model to calculate the delay of content generation. After that, we propose a novel in-context learning method to optimize the task offloading decisions. It utilizes LLM's inference capabilities, and avoids the difficulty of dedicated model training or fine-tuning as in conventional machine learning algorithms. Finally, the simulations demonstrate that the proposed edge-cloud deployment and in-context learning task offloading method can achieve satisfactory generation service quality without dedicated model training or fine-tuning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02549v1-abstract-full').style.display = 'none'; document.getElementById('2408.02549v1-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 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/2407.21507">arXiv:2407.21507</a> <span> [<a href="https://arxiv.org/pdf/2407.21507">pdf</a>, <a href="https://arxiv.org/format/2407.21507">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="Machine Learning">cs.LG</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"> FSSC: Federated Learning of Transformer Neural Networks for Semantic Image Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yan%2C+Y">Yuna Yan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xin Zhang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+L">Lixin Li</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+W">Wensheng Lin</a>, <a href="/search/eess?searchtype=author&query=Li%2C+R">Rui Li</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+W">Wenchi Cheng</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="2407.21507v1-abstract-short" style="display: inline;"> In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.21507v1-abstract-full').style.display = 'inline'; document.getElementById('2407.21507v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.21507v1-abstract-full" style="display: none;"> In this paper, we address the problem of image semantic communication in a multi-user deployment scenario and propose a federated learning (FL) strategy for a Swin Transformer-based semantic communication system (FSSC). Firstly, we demonstrate that the adoption of a Swin Transformer for joint source-channel coding (JSCC) effectively extracts semantic information in the communication system. Next, the FL framework is introduced to collaboratively learn a global model by aggregating local model parameters, rather than directly sharing clients' data. This approach enhances user privacy protection and reduces the workload on the server or mobile edge. Simulation evaluations indicate that our method outperforms the typical JSCC algorithm and traditional separate-based communication algorithms. Particularly after integrating local semantics, the global aggregation model has further increased the Peak Signal-to-Noise Ratio (PSNR) by more than 2dB, thoroughly proving the effectiveness of our algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.21507v1-abstract-full').style.display = 'none'; document.getElementById('2407.21507v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.14651">arXiv:2407.14651</a> <span> [<a href="https://arxiv.org/pdf/2407.14651">pdf</a>, <a href="https://arxiv.org/format/2407.14651">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> <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"> Improving Representation of High-frequency Components for Medical Foundation Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chu%2C+Y">Yuetan Chu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yilan Zhang</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhongyi Han</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+C">Changchun Yang</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+L">Longxi Zhou</a>, <a href="/search/eess?searchtype=author&query=Luo%2C+G">Gongning Luo</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+X">Xin Gao</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="2407.14651v2-abstract-short" style="display: inline;"> Foundation models have recently attracted significant attention for their impressive generalizability across diverse downstream tasks. However, these models are demonstrated to exhibit great limitations in representing high-frequency components and fine-grained details. In many medical imaging tasks, the precise representation of such information is crucial due to the inherently intricate anatomic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.14651v2-abstract-full').style.display = 'inline'; document.getElementById('2407.14651v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.14651v2-abstract-full" style="display: none;"> Foundation models have recently attracted significant attention for their impressive generalizability across diverse downstream tasks. However, these models are demonstrated to exhibit great limitations in representing high-frequency components and fine-grained details. In many medical imaging tasks, the precise representation of such information is crucial due to the inherently intricate anatomical structures, sub-visual features, and complex boundaries involved. Consequently, the limited representation of prevalent foundation models can result in significant performance degradation or even failure in these tasks. To address these challenges, we propose a novel pretraining strategy, named Frequency-advanced Representation Autoencoder (Frepa). Through high-frequency masking and low-frequency perturbation combined with adversarial learning, Frepa encourages the encoder to effectively represent and preserve high-frequency components in the image embeddings. Additionally, we introduce an innovative histogram-equalized image masking strategy, extending the Masked Autoencoder approach beyond ViT to other architectures such as Swin Transformer and convolutional networks. We develop Frepa across nine medical modalities and validate it on 32 downstream tasks for both 2D images and 3D volume data. Without fine-tuning, Frepa can outperform other self-supervised pretraining methods and, in some cases, even surpasses task-specific trained models. This improvement is particularly significant for tasks involving fine-grained details, such as achieving up to a +15% increase in DSC for retina vessel segmentation and a +7% increase in IoU for lung nodule detection. Further experiments quantitatively reveal that Frepa enables superior high-frequency representations and preservation in the embeddings, underscoring its potential for developing more generalized and universal medical image foundation models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.14651v2-abstract-full').style.display = 'none'; document.getElementById('2407.14651v2-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.19072">arXiv:2406.19072</a> <span> [<a href="https://arxiv.org/pdf/2406.19072">pdf</a>, <a href="https://arxiv.org/format/2406.19072">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"> Scatterer Recognition for Multi-Modal Intelligent Vehicular Channel Modeling via Synesthesia of Machines </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Huang%2C+Z">Ziwei Huang</a>, <a href="/search/eess?searchtype=author&query=Bai%2C+L">Lu Bai</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zengrui Han</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+X">Xiang Cheng</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="2406.19072v2-abstract-short" style="display: inline;"> In this paper, a novel multi-modal intelligent vehicular channel model is proposed by scatterer recognition from light detection and ranging (LiDAR) point clouds via Synesthesia of Machines (SoM). The proposed model can support the design of intelligent transportation systems (ITSs). To provide a robust data foundation, a new intelligent sensing-communication integration dataset in vehicular urban… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.19072v2-abstract-full').style.display = 'inline'; document.getElementById('2406.19072v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.19072v2-abstract-full" style="display: none;"> In this paper, a novel multi-modal intelligent vehicular channel model is proposed by scatterer recognition from light detection and ranging (LiDAR) point clouds via Synesthesia of Machines (SoM). The proposed model can support the design of intelligent transportation systems (ITSs). To provide a robust data foundation, a new intelligent sensing-communication integration dataset in vehicular urban scenarios is constructed. Based on the constructed dataset, the complex SoM mechanism, i.e., mapping relationship between scatterers in electromagnetic space and LiDAR point clouds in physical environment, is explored via multilayer perceptron (MLP) in consideration of electromagnetic propagation mechanism. By using LiDAR point clouds to implement scatterer recognition, channel non-stationarity and consistency are captured closely coupled with the environment. Using ray-tracing (RT)-based results as the ground truth, the scatterer recognition accuracy exceeds 90%. The accuracy of the proposed model is further verified by the close fit between simulation results and RT results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.19072v2-abstract-full').style.display = 'none'; document.getElementById('2406.19072v2-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 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/2406.16994">arXiv:2406.16994</a> <span> [<a href="https://arxiv.org/pdf/2406.16994">pdf</a>, <a href="https://arxiv.org/format/2406.16994">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kim%2C+G+S">Gyu Seon Kim</a>, <a href="/search/eess?searchtype=author&query=Cho%2C+Y">Yeryeong Cho</a>, <a href="/search/eess?searchtype=author&query=Chung%2C+J">Jaehyun Chung</a>, <a href="/search/eess?searchtype=author&query=Park%2C+S">Soohyun Park</a>, <a href="/search/eess?searchtype=author&query=Jung%2C+S">Soyi Jung</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Kim%2C+J">Joongheon Kim</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="2406.16994v1-abstract-short" style="display: inline;"> Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (e.g., polar regions) and the energy efficiency limitations in CubeSats. To tackle these problems, high-altitude long-endurance unmanned aerial vehicles (HALE-UAVs) can complement these CubeSat shortcomings for prov… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.16994v1-abstract-full').style.display = 'inline'; document.getElementById('2406.16994v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.16994v1-abstract-full" style="display: none;"> Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (e.g., polar regions) and the energy efficiency limitations in CubeSats. To tackle these problems, high-altitude long-endurance unmanned aerial vehicles (HALE-UAVs) can complement these CubeSat shortcomings for providing cooperatively global access sustainability and energy efficiency. However, as the number of CubeSats and HALE-UAVs, increases, the scheduling dimension of each ground station (GS) increases. As a result, each GS can fall into the curse of dimensionality, and this challenge becomes one major hurdle for efficient global access. Therefore, this paper provides a quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency. The main reason why the QMARL-based scheduler can be beneficial is that the algorithm facilitates a logarithmic-scale reduction in scheduling action dimensions, which is one critical feature as the number of CubeSats and HALE-UAVs expands. Additionally, individual GSs have different traffic demands depending on their locations and characteristics, thus it is essential to provide differentiated access services. The superiority of the proposed scheduler is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.16994v1-abstract-full').style.display = 'none'; document.getElementById('2406.16994v1-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">17 pages, 22 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/2406.13335">arXiv:2406.13335</a> <span> [<a href="https://arxiv.org/pdf/2406.13335">pdf</a>, <a href="https://arxiv.org/format/2406.13335">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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cao%2C+X">Xuelin Cao</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+B">Bo Yang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+K">Kaining Wang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+X">Xinghua Li</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+Z">Zhiwen Yu</a>, <a href="/search/eess?searchtype=author&query=Yuen%2C+C">Chau Yuen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yan Zhang</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="2406.13335v1-abstract-short" style="display: inline;"> With the rapidly increasing number of bandwidth-intensive terminals capable of intelligent computing and communication, such as smart devices equipped with shallow neural network models, the complexity of multiple access for these intelligent terminals is increasing due to the dynamic network environment and ubiquitous connectivity in 6G systems. Traditional multiple access (MA) design and optimiz… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13335v1-abstract-full').style.display = 'inline'; document.getElementById('2406.13335v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.13335v1-abstract-full" style="display: none;"> With the rapidly increasing number of bandwidth-intensive terminals capable of intelligent computing and communication, such as smart devices equipped with shallow neural network models, the complexity of multiple access for these intelligent terminals is increasing due to the dynamic network environment and ubiquitous connectivity in 6G systems. Traditional multiple access (MA) design and optimization methods are gradually losing ground to artificial intelligence (AI) techniques that have proven their superiority in handling complexity. AI-empowered MA and its optimization strategies aimed at achieving high Quality-of-Service (QoS) are attracting more attention, especially in the area of latency-sensitive applications in 6G systems. In this work, we aim to: 1) present the development and comparative evaluation of AI-enabled MA; 2) provide a timely survey focusing on spectrum sensing, protocol design, and optimization for AI-empowered MA; and 3) explore the potential use cases of AI-empowered MA in the typical application scenarios within 6G systems. Specifically, we first present a unified framework of AI-empowered MA for 6G systems by incorporating various promising machine learning techniques in spectrum sensing, resource allocation, MA protocol design, and optimization. We then introduce AI-empowered MA spectrum sensing related to spectrum sharing and spectrum interference management. Next, we discuss the AI-empowered MA protocol designs and implementation methods by reviewing and comparing the state-of-the-art, and we further explore the optimization algorithms related to dynamic resource management, parameter adjustment, and access scheme switching. Finally, we discuss the current challenges, point out open issues, and outline potential future research directions in this field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.13335v1-abstract-full').style.display = 'none'; document.getElementById('2406.13335v1-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 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/2406.11918">arXiv:2406.11918</a> <span> [<a href="https://arxiv.org/pdf/2406.11918">pdf</a>, <a href="https://arxiv.org/format/2406.11918">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"> QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing: An Online Joint Optimization Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=He%2C+L">Long He</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+G">Geng Sun</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Z">Zemin Sun</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Q">Qingqing Wu</a>, <a href="/search/eess?searchtype=author&query=Kang%2C+J">Jiawen Kang</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Leung%2C+V+C+M">Victor C. M. Leung</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="2406.11918v1-abstract-short" style="display: inline;"> In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, unmanned aerial vehicle (UAV)-assisted MEC is emerging as a new promising paradigm to provide edge computing services for ground… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11918v1-abstract-full').style.display = 'inline'; document.getElementById('2406.11918v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.11918v1-abstract-full" style="display: none;"> In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, unmanned aerial vehicle (UAV)-assisted MEC is emerging as a new promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy consumption constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory planning approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results provide empirical evidence supporting the superior system performance of the proposed OJTRTA in comparison to alternative approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11918v1-abstract-full').style.display = 'none'; document.getElementById('2406.11918v1-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 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/2406.08038">arXiv:2406.08038</a> <span> [<a href="https://arxiv.org/pdf/2406.08038">pdf</a>, <a href="https://arxiv.org/format/2406.08038">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"> Interference Analysis for Coexistence of UAVs and Civil Aircrafts Based on Automatic Dependent Surveillance-Broadcast </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liao%2C+Y">Yiyang Liao</a>, <a href="/search/eess?searchtype=author&query=Jia%2C+Z">Ziye Jia</a>, <a href="/search/eess?searchtype=author&query=Dong%2C+C">Chao Dong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+L">Lei Zhang</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Q">Qihui Wu</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+H">Huiling Hu</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="2406.08038v1-abstract-short" style="display: inline;"> Due to the advantages of high mobility and easy deployment, unmanned aerial vehicles (UAVs) are widely applied in both military and civilian fields. In order to strengthen the flight surveillance of UAVs and guarantee the airspace safety, UAVs can be equipped with the automatic dependent surveillance-broadcast (ADS-B) system, which periodically sends flight information to other aircrafts and groun… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08038v1-abstract-full').style.display = 'inline'; document.getElementById('2406.08038v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.08038v1-abstract-full" style="display: none;"> Due to the advantages of high mobility and easy deployment, unmanned aerial vehicles (UAVs) are widely applied in both military and civilian fields. In order to strengthen the flight surveillance of UAVs and guarantee the airspace safety, UAVs can be equipped with the automatic dependent surveillance-broadcast (ADS-B) system, which periodically sends flight information to other aircrafts and ground stations (GSs). However, due to the limited resource of channel capacity, UAVs equipped with ADS-B results in the interference between UAVs and civil aircrafts (CAs), which further impacts the accuracy of received information at GSs. In detail, the channel capacity is mainly affected by the density of aircrafts and the transmitting power of ADS-B. Hence, based on the three-dimensional poisson point process, this work leverages the stochastic geometry theory to build a model of the coexistence of UAVs and CAs and analyze the interference performance of ADS-B monitoring system. From simulation results, we reveal the effects of transmitting power, density, threshold and pathloss on the performance of the ADS-B monitoring system. Besides, we provide the suggested transmitting power and density for the safe coexistence of UAVs and CAs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08038v1-abstract-full').style.display = 'none'; document.getElementById('2406.08038v1-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 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/2406.05780">arXiv:2406.05780</a> <span> [<a href="https://arxiv.org/pdf/2406.05780">pdf</a>, <a href="https://arxiv.org/ps/2406.05780">ps</a>, <a href="https://arxiv.org/format/2406.05780">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"> Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-Player Bandits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tong%2C+J">Jingwen Tong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+H">Hongliang Zhang</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+L">Liqun Fu</a>, <a href="/search/eess?searchtype=author&query=Leshem%2C+A">Amir Leshem</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="2406.05780v1-abstract-short" style="display: inline;"> This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable intelligent surface (RIS) assisted cellular network. The objective is to maximize the sum rate of all IoT devices by finding the optimal RIS and spreading factor (SF) for each device. Since these IoT devices lack prior inf… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.05780v1-abstract-full').style.display = 'inline'; document.getElementById('2406.05780v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.05780v1-abstract-full" style="display: none;"> This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable intelligent surface (RIS) assisted cellular network. The objective is to maximize the sum rate of all IoT devices by finding the optimal RIS and spreading factor (SF) for each device. Since these IoT devices lack prior information on the RISs or the channel state information (CSI), a distributed resource allocation framework with low complexity and learning features is required to achieve this goal. Therefore, we model this problem as a two-stage multi-player multi-armed bandit (MPMAB) framework to learn the optimal RIS and SF sequentially. Then, we put forth an exploration and exploitation boosting (E2Boost) algorithm to solve this two-stage MPMAB problem by combining the $蔚$-greedy algorithm, Thompson sampling (TS) algorithm, and non-cooperation game method. We derive an upper regret bound for the proposed algorithm, i.e., $\mathcal{O}(\log^{1+未}_2 T)$, increasing logarithmically with the time horizon $T$. Numerical results show that the E2Boost algorithm has the best performance among the existing methods and exhibits a fast convergence rate. More importantly, the proposed algorithm is not sensitive to the number of combinations of the RISs and SFs thanks to the two-stage allocation mechanism, which can benefit high-density networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.05780v1-abstract-full').style.display = 'none'; document.getElementById('2406.05780v1-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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 paper was published in IEEE Transcation on Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.02000">arXiv:2406.02000</a> <span> [<a href="https://arxiv.org/pdf/2406.02000">pdf</a>, <a href="https://arxiv.org/format/2406.02000">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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Advancing Ultra-Reliable 6G: Transformer and Semantic Localization Empowered Robust Beamforming in Millimeter-Wave Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Raha%2C+A+D">Avi Deb Raha</a>, <a href="/search/eess?searchtype=author&query=Kim%2C+K">Kitae Kim</a>, <a href="/search/eess?searchtype=author&query=Adhikary%2C+A">Apurba Adhikary</a>, <a href="/search/eess?searchtype=author&query=Gain%2C+M">Mrityunjoy Gain</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Hong%2C+C+S">Choong Seon Hong</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="2406.02000v3-abstract-short" style="display: inline;"> Advancements in 6G wireless technology have elevated the importance of beamforming, especially for attaining ultra-high data rates via millimeter-wave (mmWave) frequency deployment. Although promising, mmWave bands require substantial beam training to achieve precise beamforming. While initial deep learning models that use RGB camera images demonstrated promise in reducing beam training overhead,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02000v3-abstract-full').style.display = 'inline'; document.getElementById('2406.02000v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.02000v3-abstract-full" style="display: none;"> Advancements in 6G wireless technology have elevated the importance of beamforming, especially for attaining ultra-high data rates via millimeter-wave (mmWave) frequency deployment. Although promising, mmWave bands require substantial beam training to achieve precise beamforming. While initial deep learning models that use RGB camera images demonstrated promise in reducing beam training overhead, their performance suffers due to sensitivity to lighting and environmental variations. Due to this sensitivity, Quality of Service (QoS) fluctuates, eventually affecting the stability and dependability of networks in dynamic environments. This emphasizes a critical need for robust solutions. This paper proposes a robust beamforming technique to ensure consistent QoS under varying environmental conditions. An optimization problem has been formulated to maximize users' data rates. To solve the formulated NP-hard optimization problem, we decompose it into two subproblems: the semantic localization problem and the optimal beam selection problem. To solve the semantic localization problem, we propose a novel method that leverages the K-means clustering and YOLOv8 model. To solve the beam selection problem, we propose a novel lightweight hybrid architecture that combines a lightweight transformer with a CNN architecture through a weighted entropy mechanism. This hybrid architecture utilizes multimodal data sources to dynamically predict the optimal beams. A novel metric, Accuracy-Complexity Efficiency (ACE), has been proposed to quantify this. Six testing scenarios have been developed to evaluate the robustness of the proposed model. Finally, the simulation result demonstrates that the proposed model outperforms several state-of-the-art baselines regarding beam prediction accuracy, received power, and ACE in the developed test scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02000v3-abstract-full').style.display = 'none'; document.getElementById('2406.02000v3-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 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/2405.20595">arXiv:2405.20595</a> <span> [<a href="https://arxiv.org/pdf/2405.20595">pdf</a>, <a href="https://arxiv.org/format/2405.20595">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"> Multi-Beam Integrated Sensing and Communication: State-of-the-Art, Challenges and Opportunities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhuo%2C+Y">Yinxiao Zhuo</a>, <a href="/search/eess?searchtype=author&query=Mao%2C+T">Tianqi Mao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+H">Haojin Li</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+C">Chen Sun</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zhaocheng Wang</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+S">Sheng Chen</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="2405.20595v1-abstract-short" style="display: inline;"> Integrated sensing and communication (ISAC) has been envisioned as a critical enabling technology for the next-generation wireless communication, which can realize location/motion detection of surroundings with communication devices. This additional sensing capability leads to a substantial network quality gain and expansion of the service scenarios. As the system evolves to millimeter wave (mmWav… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20595v1-abstract-full').style.display = 'inline'; document.getElementById('2405.20595v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.20595v1-abstract-full" style="display: none;"> Integrated sensing and communication (ISAC) has been envisioned as a critical enabling technology for the next-generation wireless communication, which can realize location/motion detection of surroundings with communication devices. This additional sensing capability leads to a substantial network quality gain and expansion of the service scenarios. As the system evolves to millimeter wave (mmWave) and above, ISAC can realize simultaneous communications and sensing of the ultra-high throughput level and radar resolution with compact design, which relies on directional beamforming against the path loss. With the multi-beam technology, the dual functions of ISAC can be seamlessly incorporated at the beamspace level by unleashing the potential of joint beamforming. To this end, this article investigates the key technologies for multi-beam ISAC system. We begin with an overview of the current state-of-the-art solutions in multi-beam ISAC. Subsequently, a detailed analysis of the advantages associated with the multi-beam ISAC is provided. Additionally, the key technologies for transmitter, channel and receiver of the multi-beam ISAC are introduced. Finally, we explore the challenges and opportunities presented by multi-beam ISAC, offering valuable insights into this emerging field. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20595v1-abstract-full').style.display = 'none'; document.getElementById('2405.20595v1-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 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/2405.19771">arXiv:2405.19771</a> <span> [<a href="https://arxiv.org/pdf/2405.19771">pdf</a>, <a href="https://arxiv.org/format/2405.19771">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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Data Service Maximization in Space-Air-Ground Integrated 6G Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ei%2C+N+N">Nway Nway Ei</a>, <a href="/search/eess?searchtype=author&query=Kim%2C+K">Kitae Kim</a>, <a href="/search/eess?searchtype=author&query=Tun%2C+Y+K">Yan Kyaw Tun</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Hong%2C+C+S">Choong Seon Hong</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="2405.19771v2-abstract-short" style="display: inline;"> Integrating terrestrial and non-terrestrial networks has emerged as a promising paradigm to fulfill the constantly growing demand for connectivity, low transmission delay, and quality of services (QoS). This integration brings together the strengths of the reliability of terrestrial networks, broad coverage and service continuity of non-terrestrial networks like low earth orbit satellites (LEOSats… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.19771v2-abstract-full').style.display = 'inline'; document.getElementById('2405.19771v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.19771v2-abstract-full" style="display: none;"> Integrating terrestrial and non-terrestrial networks has emerged as a promising paradigm to fulfill the constantly growing demand for connectivity, low transmission delay, and quality of services (QoS). This integration brings together the strengths of the reliability of terrestrial networks, broad coverage and service continuity of non-terrestrial networks like low earth orbit satellites (LEOSats), etc. In this work, we study a data service maximization problem in space-air-ground integrated network (SAGIN) where the ground base stations (GBSs) and LEOSats cooperatively serve the coexisting aerial users (AUs) and ground users (GUs). Then, by considering the spectrum scarcity, interference, and QoS requirements of the users, we jointly optimize the user association, AU's trajectory, and power allocation. To tackle the formulated mixed-integer non-convex problem, we disintegrate it into two subproblems: 1) user association problem and 2) trajectory and power allocation problem. We formulate the user association problem as a binary integer programming problem and solve it by using the Gurobi optimizer. Meanwhile, the trajectory and power allocation problem is solved by the deep deterministic policy gradient (DDPG) method to cope with the problem's non-convexity and dynamic network environments. Then, the two subproblems are alternately solved by the proposed block coordinate descent algorithm. By comparing with the baselines in the existing literature, extensive simulations are conducted to evaluate the performance of the proposed framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.19771v2-abstract-full').style.display = 'none'; document.getElementById('2405.19771v2-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 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">5 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/2405.18844">arXiv:2405.18844</a> <span> [<a href="https://arxiv.org/pdf/2405.18844">pdf</a>, <a href="https://arxiv.org/format/2405.18844">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"> Optical IRS for Visible Light Communication: Modeling, Design, and Open Issues </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sun%2C+S">Shiyuan Sun</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+F">Fang Yang</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</a>, <a href="/search/eess?searchtype=author&query=Song%2C+J">Jian Song</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rui 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="2405.18844v1-abstract-short" style="display: inline;"> Optical intelligent reflecting surface (OIRS) offers a new and effective approach to resolving the line-of-sight blockage issue in visible light communication (VLC) by enabling redirection of light to bypass obstacles, thereby dramatically enhancing indoor VLC coverage and reliability. This article provides a comprehensive overview of OIRS for VLC, including channel modeling, design techniques, an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18844v1-abstract-full').style.display = 'inline'; document.getElementById('2405.18844v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.18844v1-abstract-full" style="display: none;"> Optical intelligent reflecting surface (OIRS) offers a new and effective approach to resolving the line-of-sight blockage issue in visible light communication (VLC) by enabling redirection of light to bypass obstacles, thereby dramatically enhancing indoor VLC coverage and reliability. This article provides a comprehensive overview of OIRS for VLC, including channel modeling, design techniques, and open issues. First, we present the characteristics of OIRS-reflected channels and introduce two practical models, namely, optics model and association model, which are then compared in terms of applicable conditions, configuration methods, and channel parameters. Next, under the more practically appealing association model, we discuss the main design techniques for OIRS-aided VLC systems, including beam alignment, channel estimation, and OIRS reflection optimization. Finally, open issues are identified to stimulate future research in this area. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18844v1-abstract-full').style.display = 'none'; document.getElementById('2405.18844v1-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 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/2405.18167">arXiv:2405.18167</a> <span> [<a href="https://arxiv.org/pdf/2405.18167">pdf</a>, <a href="https://arxiv.org/format/2405.18167">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"> Confidence-aware multi-modality learning for eye disease screening </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zou%2C+K">Ke Zou</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+T">Tian Lin</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zongbo Han</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+M">Meng Wang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xuedong Yuan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+H">Haoyu Chen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+C">Changqing Zhang</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+X">Xiaojing Shen</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+H">Huazhu Fu</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="2405.18167v1-abstract-short" style="display: inline;"> Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performances. However, recent improvements have mainly focused on accuracy, often neglecting the importance of confidence and robustness in predictions for diverse modalities. In this study, we propose a novel multi-modality evi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18167v1-abstract-full').style.display = 'inline'; document.getElementById('2405.18167v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.18167v1-abstract-full" style="display: none;"> Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performances. However, recent improvements have mainly focused on accuracy, often neglecting the importance of confidence and robustness in predictions for diverse modalities. In this study, we propose a novel multi-modality evidential fusion pipeline for eye disease screening. It provides a measure of confidence for each modality and elegantly integrates the multi-modality information using a multi-distribution fusion perspective. Specifically, our method first utilizes normal inverse gamma prior distributions over pre-trained models to learn both aleatoric and epistemic uncertainty for uni-modality. Then, the normal inverse gamma distribution is analyzed as the Student's t distribution. Furthermore, within a confidence-aware fusion framework, we propose a mixture of Student's t distributions to effectively integrate different modalities, imparting the model with heavy-tailed properties and enhancing its robustness and reliability. More importantly, the confidence-aware multi-modality ranking regularization term induces the model to more reasonably rank the noisy single-modal and fused-modal confidence, leading to improved reliability and accuracy. Experimental results on both public and internal datasets demonstrate that our model excels in robustness, particularly in challenging scenarios involving Gaussian noise and modality missing conditions. Moreover, our model exhibits strong generalization capabilities to out-of-distribution data, underscoring its potential as a promising solution for multimodal eye disease screening. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18167v1-abstract-full').style.display = 'none'; document.getElementById('2405.18167v1-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, 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">27 pages, 7 figures, 9 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.12580">arXiv:2405.12580</a> <span> [<a href="https://arxiv.org/pdf/2405.12580">pdf</a>, <a href="https://arxiv.org/format/2405.12580">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"> Hybrid Digital-Analog 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=Han%2C+Z">Zhu Han</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="2405.12580v2-abstract-short" style="display: inline;"> Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digital-analog approach for multimedia transmission. This is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.12580v2-abstract-full').style.display = 'inline'; document.getElementById('2405.12580v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.12580v2-abstract-full" style="display: none;"> Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digital-analog approach for multimedia transmission. This is achieved through the introduction of digital-analog allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.12580v2-abstract-full').style.display = 'none'; document.getElementById('2405.12580v2-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 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">13 pages, 8 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/2405.07033">arXiv:2405.07033</a> <span> [<a href="https://arxiv.org/pdf/2405.07033">pdf</a>, <a href="https://arxiv.org/ps/2405.07033">ps</a>, <a href="https://arxiv.org/format/2405.07033">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="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</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"> A Performance Analysis Modeling Framework for Extended Reality Applications in Edge-Assisted Wireless Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Mallik%2C+A">Anik Mallik</a>, <a href="/search/eess?searchtype=author&query=Xie%2C+J">Jiang Xie</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="2405.07033v1-abstract-short" style="display: inline;"> Extended reality (XR) is at the center of attraction in the research community due to the emergence of augmented, mixed, and virtual reality applications. The performance of such applications needs to be uptight to maintain the requirements of latency, energy consumption, and freshness of data. Therefore, a comprehensive performance analysis model is required to assess the effectiveness of an XR a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.07033v1-abstract-full').style.display = 'inline'; document.getElementById('2405.07033v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.07033v1-abstract-full" style="display: none;"> Extended reality (XR) is at the center of attraction in the research community due to the emergence of augmented, mixed, and virtual reality applications. The performance of such applications needs to be uptight to maintain the requirements of latency, energy consumption, and freshness of data. Therefore, a comprehensive performance analysis model is required to assess the effectiveness of an XR application but is challenging to design due to the dependence of the performance metrics on several difficult-to-model parameters, such as computing resources and hardware utilization of XR and edge devices, which are controlled by both their operating systems and the application itself. Moreover, the heterogeneity in devices and wireless access networks brings additional challenges in modeling. In this paper, we propose a novel modeling framework for performance analysis of XR applications considering edge-assisted wireless networks and validate the model with experimental data collected from testbeds designed specifically for XR applications. In addition, we present the challenges associated with performance analysis modeling and present methods to overcome them in detail. Finally, the performance evaluation shows that the proposed analytical model can analyze XR applications' performance with high accuracy compared to the state-of-the-art analytical models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.07033v1-abstract-full').style.display = 'none'; document.getElementById('2405.07033v1-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, 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">12 pages, 4 figures; To appear in Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS), 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/2405.06372">arXiv:2405.06372</a> <span> [<a href="https://arxiv.org/pdf/2405.06372">pdf</a>, <a href="https://arxiv.org/format/2405.06372">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"> Intelligent Duty Cycling Management and Wake-up for Energy Harvesting IoT Networks with Correlated Activity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ru%C3%ADz-Guirola%2C+D+E">David E. Ru铆z-Guirola</a>, <a href="/search/eess?searchtype=author&query=L%C3%B3pez%2C+O+L+A">Onel L. A. L贸pez</a>, <a href="/search/eess?searchtype=author&query=Montejo-S%C3%A1nchez%2C+S">Samuel Montejo-S谩nchez</a>, <a href="/search/eess?searchtype=author&query=Mayorga%2C+I+L">Israel Leyva Mayorga</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Popovski%2C+P">Petar Popovski</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="2405.06372v1-abstract-short" style="display: inline;"> This paper presents an approach for energy-neutral Internet of Things (IoT) scenarios where the IoT devices (IoTDs) rely entirely on their energy harvesting capabilities to sustain operation. We use a Markov chain to represent the operation and transmission states of the IoTDs, a modulated Poisson process to model their energy harvesting process, and a discrete-time Markov chain to model their bat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06372v1-abstract-full').style.display = 'inline'; document.getElementById('2405.06372v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.06372v1-abstract-full" style="display: none;"> This paper presents an approach for energy-neutral Internet of Things (IoT) scenarios where the IoT devices (IoTDs) rely entirely on their energy harvesting capabilities to sustain operation. We use a Markov chain to represent the operation and transmission states of the IoTDs, a modulated Poisson process to model their energy harvesting process, and a discrete-time Markov chain to model their battery state. The aim is to efficiently manage the duty cycling of the IoTDs, so as to prolong their battery life and reduce instances of low-energy availability. We propose a duty-cycling management based on K- nearest neighbors, aiming to strike a trade-off between energy efficiency and detection accuracy. This is done by incorporating spatial and temporal correlations among IoTDs' activity, as well as their energy harvesting capabilities. We also allow the base station to wake up specific IoTDs if more information about an event is needed upon initial detection. Our proposed scheme shows significant improvements in energy savings and performance, with up to 11 times lower misdetection probability and 50\% lower energy consumption for high-density scenarios compared to a random duty cycling benchmark. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.06372v1-abstract-full').style.display = 'none'; document.getElementById('2405.06372v1-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 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/2405.00056">arXiv:2405.00056</a> <span> [<a href="https://arxiv.org/pdf/2405.00056">pdf</a>, <a href="https://arxiv.org/format/2405.00056">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="Computer Science and Game Theory">cs.GT</span> </div> </div> <p class="title is-5 mathjax"> Age of Information Minimization using Multi-agent UAVs based on AI-Enhanced Mean Field Resource Allocation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Emami%2C+Y">Yousef Emami</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+H">Hao Gao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+K">Kai Li</a>, <a href="/search/eess?searchtype=author&query=Almeida%2C+L">Luis Almeida</a>, <a href="/search/eess?searchtype=author&query=Tovar%2C+E">Eduardo Tovar</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="2405.00056v2-abstract-short" style="display: inline;"> Unmanned Aerial Vehicle (UAV) swarms play an effective role in timely data collection from ground sensors in remote and hostile areas. Optimizing the collective behavior of swarms can improve data collection performance. This paper puts forth a new mean field flight resource allocation optimization to minimize age of information (AoI) of sensory data, where balancing the trade-off between the UAVs… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00056v2-abstract-full').style.display = 'inline'; document.getElementById('2405.00056v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.00056v2-abstract-full" style="display: none;"> Unmanned Aerial Vehicle (UAV) swarms play an effective role in timely data collection from ground sensors in remote and hostile areas. Optimizing the collective behavior of swarms can improve data collection performance. This paper puts forth a new mean field flight resource allocation optimization to minimize age of information (AoI) of sensory data, where balancing the trade-off between the UAVs movements and AoI is formulated as a mean field game (MFG). The MFG optimization yields an expansive solution space encompassing continuous state and action, resulting in significant computational complexity. To address practical situations, we propose, a new mean field hybrid proximal policy optimization (MF-HPPO) scheme to minimize the average AoI by optimizing the UAV's trajectories and data collection scheduling of the ground sensors given mixed continuous and discrete actions. Furthermore, a long short term memory (LSTM) is leveraged in MF-HPPO to predict the time-varying network state and stabilize the training. Numerical results demonstrate that the proposed MF-HPPO reduces the average AoI by up to 45 percent and 57 percent in the considered simulation setting, as compared to multi-agent deep Q-learning (MADQN) method and non-learning random algorithm, respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00056v2-abstract-full').style.display = 'none'; document.getElementById('2405.00056v2-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> 2 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 April, 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">13 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:2312.09953</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 00 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> C.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.15992">arXiv:2404.15992</a> <span> [<a href="https://arxiv.org/pdf/2404.15992">pdf</a>, <a href="https://arxiv.org/format/2404.15992">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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> GAN-HA: A generative adversarial network with a novel heterogeneous dual-discriminator network and a new attention-based fusion strategy for infrared and visible image fusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lu%2C+G">Guosheng Lu</a>, <a href="/search/eess?searchtype=author&query=Fang%2C+Z">Zile Fang</a>, <a href="/search/eess?searchtype=author&query=Tian%2C+J">Jiaju Tian</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+H">Haowen Huang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yuelong Xu</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhuolin Han</a>, <a href="/search/eess?searchtype=author&query=Kang%2C+Y">Yaoming Kang</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+C">Can Feng</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+Z">Zhigang 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="2404.15992v3-abstract-short" style="display: inline;"> Infrared and visible image fusion (IVIF) aims to preserve thermal radiation information from infrared images while integrating texture details from visible images. Thermal radiation information is mainly expressed through image intensities, while texture details are typically expressed through image gradients. However, existing dual-discriminator generative adversarial networks (GANs) often rely o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.15992v3-abstract-full').style.display = 'inline'; document.getElementById('2404.15992v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.15992v3-abstract-full" style="display: none;"> Infrared and visible image fusion (IVIF) aims to preserve thermal radiation information from infrared images while integrating texture details from visible images. Thermal radiation information is mainly expressed through image intensities, while texture details are typically expressed through image gradients. However, existing dual-discriminator generative adversarial networks (GANs) often rely on two structurally identical discriminators for learning, which do not fully account for the distinct learning needs of infrared and visible image information. To this end, this paper proposes a novel GAN with a heterogeneous dual-discriminator network and an attention-based fusion strategy (GAN-HA). Specifically, recognizing the intrinsic differences between infrared and visible images, we propose, for the first time, a novel heterogeneous dual-discriminator network to simultaneously capture thermal radiation information and texture details. The two discriminators in this network are structurally different, including a salient discriminator for infrared images and a detailed discriminator for visible images. They are able to learn rich image intensity information and image gradient information, respectively. In addition, a new attention-based fusion strategy is designed in the generator to appropriately emphasize the learned information from different source images, thereby improving the information representation ability of the fusion result. In this way, the fused images generated by GAN-HA can more effectively maintain both the salience of thermal targets and the sharpness of textures. Extensive experiments on various public datasets demonstrate the superiority of GAN-HA over other state-of-the-art (SOTA) algorithms while showcasing its higher potential for practical applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.15992v3-abstract-full').style.display = 'none'; document.getElementById('2404.15992v3-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> 2 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 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/2404.14778">arXiv:2404.14778</a> <span> [<a href="https://arxiv.org/pdf/2404.14778">pdf</a>, <a href="https://arxiv.org/format/2404.14778">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"> Channel Estimation for Optical Intelligent Reflecting Surface-Assisted VLC System: A Joint Space-Time Sampling Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sun%2C+S">Shiyuan Sun</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+F">Fang Yang</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</a>, <a href="/search/eess?searchtype=author&query=Song%2C+J">Jian Song</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rui 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="2404.14778v1-abstract-short" style="display: inline;"> Optical intelligent reflecting surface (OIRS) has attracted increasing attention due to its capability of overcoming signal blockages in visible light communication (VLC), an emerging technology for the next-generation advanced transceivers. However, current works on OIRS predominantly assume known channel state information (CSI), which is essential to practical OIRS configuration. To bridge such… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14778v1-abstract-full').style.display = 'inline'; document.getElementById('2404.14778v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.14778v1-abstract-full" style="display: none;"> Optical intelligent reflecting surface (OIRS) has attracted increasing attention due to its capability of overcoming signal blockages in visible light communication (VLC), an emerging technology for the next-generation advanced transceivers. However, current works on OIRS predominantly assume known channel state information (CSI), which is essential to practical OIRS configuration. To bridge such a gap, this paper proposes a new and customized channel estimation protocol for OIRSs under the alignment-based channel model. Specifically, we first unveil OIRS spatial and temporal coherence characteristics and derive the coherence distance and the coherence time in closed form. Next, to achieve fast beam alignment over different coherence time, we propose to dynamically tune the rotational angles of the OIRS reflecting elements following a geometric optics-based non-uniform codebook. Given the above beam alignment, we propose an efficient joint space-time sampling-based algorithm to estimate the OIRS channel. In particular, we divide the OIRS into multiple subarrays based on the coherence distance and sequentially estimate their associated CSI, followed by a spacetime interpolation to retrieve full CSI for other non-aligned transceiver antennas. Numerical results validate our theoretical analyses and demonstrate the efficacy of our proposed OIRS channel estimation scheme as compared to other benchmark schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14778v1-abstract-full').style.display = 'none'; document.getElementById('2404.14778v1-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 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/2404.14706">arXiv:2404.14706</a> <span> [<a href="https://arxiv.org/pdf/2404.14706">pdf</a>, <a href="https://arxiv.org/format/2404.14706">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"> Channel Estimation for Optical IRS-Assisted VLC System via Spatial Coherence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sun%2C+S">Shiyuan Sun</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+F">Fang Yang</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</a>, <a href="/search/eess?searchtype=author&query=Song%2C+J">Jian Song</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rui 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="2404.14706v1-abstract-short" style="display: inline;"> Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14706v1-abstract-full').style.display = 'inline'; document.getElementById('2404.14706v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.14706v1-abstract-full" style="display: none;"> Optical intelligent reflecting surface (OIRS) has been considered a promising technology for visible light communication (VLC) by constructing visual line-of-sight propagation paths to address the signal blockage issue. However, the existing works on OIRSs are mostly based on perfect channel state information (CSI), whose acquisition appears to be challenging due to the passive nature of the OIRS. To tackle this challenge, this paper proposes a customized channel estimation algorithm for OIRSs. Specifically, we first unveil the OIRS spatial coherence characteristics and derive the coherence distance in closed form. Based on this property, a spatial sampling-based algorithm is proposed to estimate the OIRS-reflected channel, by dividing the OIRS into multiple subarrays based on the coherence distance and sequentially estimating their associated CSI, followed by an interpolation to retrieve the full CSI. Simulation results validate the derived OIRS spatial coherence and demonstrate the efficacy of the proposed OIRS channel estimation algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14706v1-abstract-full').style.display = 'none'; document.getElementById('2404.14706v1-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> 22 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/2404.14140">arXiv:2404.14140</a> <span> [<a href="https://arxiv.org/pdf/2404.14140">pdf</a>, <a href="https://arxiv.org/format/2404.14140">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"> Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+J">Jiacheng Wang</a>, <a href="/search/eess?searchtype=author&query=Du%2C+H">Hongyang Du</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Z">Zehui Xiong</a>, <a href="/search/eess?searchtype=author&query=Kang%2C+J">Jiawen Kang</a>, <a href="/search/eess?searchtype=author&query=Ai%2C+B">Bo Ai</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/eess?searchtype=author&query=Kim%2C+D+I">Dong In Kim</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="2404.14140v1-abstract-short" style="display: inline;"> Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also in signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses channel state information (CSI) to estimate t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14140v1-abstract-full').style.display = 'inline'; document.getElementById('2404.14140v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.14140v1-abstract-full" style="display: none;"> Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also in signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human-induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling the detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR's time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD's accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.14140v1-abstract-full').style.display = 'none'; document.getElementById('2404.14140v1-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> 22 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/2404.10343">arXiv:2404.10343</a> <span> [<a href="https://arxiv.org/pdf/2404.10343">pdf</a>, <a href="https://arxiv.org/format/2404.10343">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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ren%2C+B">Bin Ren</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yawei Li</a>, <a href="/search/eess?searchtype=author&query=Mehta%2C+N">Nancy Mehta</a>, <a href="/search/eess?searchtype=author&query=Timofte%2C+R">Radu Timofte</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+H">Hongyuan Yu</a>, <a href="/search/eess?searchtype=author&query=Wan%2C+C">Cheng Wan</a>, <a href="/search/eess?searchtype=author&query=Hong%2C+Y">Yuxin Hong</a>, <a href="/search/eess?searchtype=author&query=Han%2C+B">Bingnan Han</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Z">Zhuoyuan Wu</a>, <a href="/search/eess?searchtype=author&query=Zou%2C+Y">Yajun Zou</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yuqing Liu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+J">Jizhe Li</a>, <a href="/search/eess?searchtype=author&query=He%2C+K">Keji He</a>, <a href="/search/eess?searchtype=author&query=Fan%2C+C">Chao Fan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+H">Heng Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xiaolin Zhang</a>, <a href="/search/eess?searchtype=author&query=Yin%2C+X">Xuanwu Yin</a>, <a href="/search/eess?searchtype=author&query=Zuo%2C+K">Kunlong Zuo</a>, <a href="/search/eess?searchtype=author&query=Liao%2C+B">Bohao Liao</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+P">Peizhe Xia</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+L">Long Peng</a>, <a href="/search/eess?searchtype=author&query=Du%2C+Z">Zhibo Du</a>, <a href="/search/eess?searchtype=author&query=Di%2C+X">Xin Di</a>, <a href="/search/eess?searchtype=author&query=Li%2C+W">Wangkai Li</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yang Wang</a> , et al. (109 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="2404.10343v2-abstract-short" style="display: inline;"> This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.10343v2-abstract-full').style.display = 'inline'; document.getElementById('2404.10343v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.10343v2-abstract-full" style="display: none;"> This paper provides a comprehensive review of the NTIRE 2024 challenge, focusing on efficient single-image super-resolution (ESR) solutions and their outcomes. The task of this challenge is to super-resolve an input image with a magnification factor of x4 based on pairs of low and corresponding high-resolution images. The primary objective is to develop networks that optimize various aspects such as runtime, parameters, and FLOPs, while still maintaining a peak signal-to-noise ratio (PSNR) of approximately 26.90 dB on the DIV2K_LSDIR_valid dataset and 26.99 dB on the DIV2K_LSDIR_test dataset. In addition, this challenge has 4 tracks including the main track (overall performance), sub-track 1 (runtime), sub-track 2 (FLOPs), and sub-track 3 (parameters). In the main track, all three metrics (ie runtime, FLOPs, and parameter count) were considered. The ranking of the main track is calculated based on a weighted sum-up of the scores of all other sub-tracks. In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking. In sub-track 2, the number of FLOPs was considered. The score calculated based on the corresponding FLOPs was used to determine the ranking. In sub-track 3, the number of parameters was considered. The score calculated based on the corresponding parameters was used to determine the ranking. RLFN is set as the baseline for efficiency measurement. The challenge had 262 registered participants, and 34 teams made valid submissions. They gauge the state-of-the-art in efficient single-image super-resolution. To facilitate the reproducibility of the challenge and enable other researchers to build upon these findings, the code and the pre-trained model of validated solutions are made publicly available at https://github.com/Amazingren/NTIRE2024_ESR/. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.10343v2-abstract-full').style.display = 'none'; document.getElementById('2404.10343v2-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">The report paper of NTIRE2024 Efficient Super-resolution, accepted by CVPRW2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.07477">arXiv:2404.07477</a> <span> [<a href="https://arxiv.org/pdf/2404.07477">pdf</a>, <a href="https://arxiv.org/format/2404.07477">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"> Integrated Sensing and Communication Under DISCO Physical-Layer Jamming Attacks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Huang%2C+H">Huan Huang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+H">Hongliang Zhang</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</a>, <a href="/search/eess?searchtype=author&query=Li%2C+J">Jun Li</a>, <a href="/search/eess?searchtype=author&query=Cai%2C+Y">Yi Cai</a>, <a href="/search/eess?searchtype=author&query=Swindlehurst%2C+A+L">A. Lee Swindlehurst</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="2404.07477v2-abstract-short" style="display: inline;"> Integrated sensing and communication (ISAC) systems traditionally presuppose that sensing and communication (S&C) channels remain approximately constant during their coherence time. However, a "DISCO" reconfigurable intelligent surface (DRIS), i.e., an illegitimate RIS with random, time-varying reflection properties that acts like a "disco ball," introduces a paradigm shift that enables active cha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.07477v2-abstract-full').style.display = 'inline'; document.getElementById('2404.07477v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.07477v2-abstract-full" style="display: none;"> Integrated sensing and communication (ISAC) systems traditionally presuppose that sensing and communication (S&C) channels remain approximately constant during their coherence time. However, a "DISCO" reconfigurable intelligent surface (DRIS), i.e., an illegitimate RIS with random, time-varying reflection properties that acts like a "disco ball," introduces a paradigm shift that enables active channel aging more rapidly during the channel coherence time. In this letter, we investigate the impact of DISCO jamming attacks launched by a DRISbased fully-passive jammer (FPJ) on an ISAC system. Specifically, an ISAC problem formulation and a corresponding waveform optimization are presented in which the ISAC waveform design considers the trade-off between the S&C performance and is formulated as a Pareto optimization problem. Moreover, a theoretical analysis is conducted to quantify the impact of DISCO jamming attacks. Numerical results are presented to evaluate the S&C performance under DISCO jamming attacks and to validate the derived theoretical analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.07477v2-abstract-full').style.display = 'none'; document.getElementById('2404.07477v2-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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 paper has been submitted for possible publication. For the code of the DISCO RIS is available on Github (https://github.com/huanhuan1799/Disco-Intelligent-Reflecting-Surfaces-Active-Channel-Aging-for-Fully-Passive-Jamming-Attacks)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.06765">arXiv:2404.06765</a> <span> [<a href="https://arxiv.org/pdf/2404.06765">pdf</a>, <a href="https://arxiv.org/format/2404.06765">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"> Harnessing the Power of AI-Generated Content for Semantic Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yiru Wang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+W">Wanting Yang</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Z">Zehui Xiong</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+Y">Yuping Zhao</a>, <a href="/search/eess?searchtype=author&query=Quek%2C+T+Q+S">Tony Q. S. Quek</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="2404.06765v1-abstract-short" style="display: inline;"> Semantic Communication (SemCom) is envisaged as the next-generation paradigm to address challenges stemming from the conflicts between the increasing volume of transmission data and the scarcity of spectrum resources. However, existing SemCom systems face drawbacks, such as low explainability, modality rigidity, and inadequate reconstruction functionality. Recognizing the transformative capabiliti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.06765v1-abstract-full').style.display = 'inline'; document.getElementById('2404.06765v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.06765v1-abstract-full" style="display: none;"> Semantic Communication (SemCom) is envisaged as the next-generation paradigm to address challenges stemming from the conflicts between the increasing volume of transmission data and the scarcity of spectrum resources. However, existing SemCom systems face drawbacks, such as low explainability, modality rigidity, and inadequate reconstruction functionality. Recognizing the transformative capabilities of AI-generated content (AIGC) technologies in content generation, this paper explores a pioneering approach by integrating them into SemCom to address the aforementioned challenges. We employ a three-layer model to illustrate the proposed AIGC-assisted SemCom (AIGC-SCM) architecture, emphasizing its clear deviation from existing SemCom. Grounded in this model, we investigate various AIGC technologies with the potential to augment SemCom's performance. In alignment with SemCom's goal of conveying semantic meanings, we also introduce the new evaluation methods for our AIGC-SCM system. Subsequently, we explore communication scenarios where our proposed AIGC-SCM can realize its potential. For practical implementation, we construct a detailed integration workflow and conduct a case study in a virtual reality image transmission scenario. The results demonstrate our ability to maintain a high degree of alignment between the reconstructed content and the original source information, while substantially minimizing the data volume required for transmission. These findings pave the way for further enhancements in communication efficiency and the improvement of Quality of Service. At last, we present future directions for AIGC-SCM studies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.06765v1-abstract-full').style.display = 'none'; document.getElementById('2404.06765v1-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 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/2404.00371">arXiv:2404.00371</a> <span> [<a href="https://arxiv.org/pdf/2404.00371">pdf</a>, <a href="https://arxiv.org/format/2404.00371">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="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/TMC.2024.3383038">10.1109/TMC.2024.3383038 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tong%2C+J">Jingwen Tong</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhenzhen Chen</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+L">Liqun Fu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jun Zhang</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="2404.00371v1-abstract-short" style="display: inline;"> Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00371v1-abstract-full').style.display = 'inline'; document.getElementById('2404.00371v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.00371v1-abstract-full" style="display: none;"> Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user experiences, evaluating the well-trained global model after the FL process is crucial. In this paper, we propose a closed-loop model analytics framework that allows for effective evaluation of the trained global model using clients' local data. To address the challenges posed by system and data heterogeneities in the FL process, we study a goal-directed client selection problem based on the model analytics framework by selecting a subset of clients for the model training. This problem is formulated as a stochastic multi-armed bandit (SMAB) problem. We first put forth a quick initial upper confidence bound (Quick-Init UCB) algorithm to solve this SMAB problem under the federated analytics (FA) framework. Then, we further propose a belief propagation-based UCB (BP-UCB) algorithm under the democratized analytics (DA) framework. Moreover, we derive two regret upper bounds for the proposed algorithms, which increase logarithmically over the time horizon. The numerical results demonstrate that the proposed algorithms achieve nearly optimal performance, with a gap of less than 1.44% and 3.12% under the FA and DA frameworks, respectively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00371v1-abstract-full').style.display = 'none'; document.getElementById('2404.00371v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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 was partly presented at IEEE ICC 2022</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 14J60 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.11071">arXiv:2403.11071</a> <span> [<a href="https://arxiv.org/pdf/2403.11071">pdf</a>, <a href="https://arxiv.org/format/2403.11071">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"> Wavenumber Domain Sparse Channel Estimation in Holographic MIMO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Guo%2C+X">Xufeng Guo</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yuanbin Chen</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Ying Wang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zhaocheng Wang</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="2403.11071v1-abstract-short" style="display: inline;"> In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially-stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angular power, which is referred to as the p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11071v1-abstract-full').style.display = 'inline'; document.getElementById('2403.11071v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.11071v1-abstract-full" style="display: none;"> In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially-stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angular power, which is referred to as the power leakage. To tackle this challenge, the HMIMO channel is represented in the wavenumber domain for exploring its cluster-dominated sparsity. Specifically, a finite set of Fourier harmonics acts as a series of sampling probes to encapsulate the integral of the power spectrum over specific angular regions. This technique effectively eliminates power leakage resulting from power mismatches induced by the use of discrete angular-domain probes. Next, the channel estimation problem is recast as a sparse recovery of the significant angular power spectrum over the continuous integration region. We then propose an accompanying graph-cut-based swap expansion (GCSE) algorithm to extract beneficial sparsity inherent in HMIMO channels. Numerical results demonstrate that this wavenumber-domainbased GCSE approach achieves robust performance with rapid convergence. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11071v1-abstract-full').style.display = 'none'; document.getElementById('2403.11071v1-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 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">This paper has been accepted in 2024 ICC</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.08931">arXiv:2403.08931</a> <span> [<a href="https://arxiv.org/pdf/2403.08931">pdf</a>, <a href="https://arxiv.org/ps/2403.08931">ps</a>, <a href="https://arxiv.org/format/2403.08931">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"> Unleashing the True Power of Age-of-Information: Service Aggregation in Connected and Autonomous Vehicles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Mallik%2C+A">Anik Mallik</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+D">Dawei Chen</a>, <a href="/search/eess?searchtype=author&query=Han%2C+K">Kyungtae Han</a>, <a href="/search/eess?searchtype=author&query=Xie%2C+J">Jiang Xie</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="2403.08931v1-abstract-short" style="display: inline;"> Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive information update services to ensure the safety of people and assets, and satisfactory entertainment applications. Therefore, the freshness of information is a crucial performance metric for CAV services. However, information from roadside sensors and nearby vehicles can get delayed in transmission due to the high mobility… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.08931v1-abstract-full').style.display = 'inline'; document.getElementById('2403.08931v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.08931v1-abstract-full" style="display: none;"> Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive information update services to ensure the safety of people and assets, and satisfactory entertainment applications. Therefore, the freshness of information is a crucial performance metric for CAV services. However, information from roadside sensors and nearby vehicles can get delayed in transmission due to the high mobility of vehicles. Our research shows that a CAV's relative distance and speed play an essential role in determining the Age-of-Information (AoI). With an increase in AoI, incremental service aggregation issues are observed with out-of-sequence information updates, which hampers the performance of low-latency applications in CAVs. In this paper, we propose a novel AoI-based service aggregation method for CAVs, which can process the information updates according to their update cycles. First, the AoI for sensors and vehicles is modeled, and a predictive AoI system is designed. Then, to reduce the overall service aggregation time and computational load, intervals are used for periodic AoI prediction, and information sources are clustered based on the AoI value. Finally, the system aggregates services for CAV applications using the predicted AoI. We evaluate the system performance based on data sequencing success rate (DSSR) and overall system latency. Lastly, we compare the performance of our proposed system with three other state-of-the-art methods. The evaluation and comparison results show that our proposed predictive AoI-based service aggregation system maintains satisfactory latency and DSSR for CAV applications and outperforms other existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.08931v1-abstract-full').style.display = 'none'; document.getElementById('2403.08931v1-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 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, 8 figures, to appear in the Proceedings of IEEE International Conference on Communications (IEEE ICC, 9-13 June 2024, Denver, CO, USA)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.05826">arXiv:2403.05826</a> <span> [<a href="https://arxiv.org/pdf/2403.05826">pdf</a>, <a href="https://arxiv.org/format/2403.05826">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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Cached Model-as-a-Resource: Provisioning Large Language Model Agents for Edge Intelligence in Space-air-ground Integrated Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xu%2C+M">Minrui Xu</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+H">Hongliang Zhang</a>, <a href="/search/eess?searchtype=author&query=Kang%2C+J">Jiawen Kang</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Z">Zehui Xiong</a>, <a href="/search/eess?searchtype=author&query=Mao%2C+S">Shiwen Mao</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="2403.05826v2-abstract-short" style="display: inline;"> Edge intelligence in space-air-ground integrated networks (SAGINs) can enable worldwide network coverage beyond geographical limitations for users to access ubiquitous and low-latency intelligence services. Facing global coverage and complex environments in SAGINs, edge intelligence can provision approximate large language models (LLMs) agents for users via edge servers at ground base stations (BS… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05826v2-abstract-full').style.display = 'inline'; document.getElementById('2403.05826v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.05826v2-abstract-full" style="display: none;"> Edge intelligence in space-air-ground integrated networks (SAGINs) can enable worldwide network coverage beyond geographical limitations for users to access ubiquitous and low-latency intelligence services. Facing global coverage and complex environments in SAGINs, edge intelligence can provision approximate large language models (LLMs) agents for users via edge servers at ground base stations (BSs) or cloud data centers relayed by satellites. As LLMs with billions of parameters are pre-trained on vast datasets, LLM agents have few-shot learning capabilities, e.g., chain-of-thought (CoT) prompting for complex tasks, which raises a new trade-off between resource consumption and performance in SAGINs. In this paper, we propose a joint caching and inference framework for edge intelligence to provision sustainable and ubiquitous LLM agents in SAGINs. We introduce "cached model-as-a-resource" for offering LLMs with limited context windows and propose a novel optimization framework, i.e., joint model caching and inference, to utilize cached model resources for provisioning LLM agent services along with communication, computing, and storage resources. We design "age of thought" (AoT) considering the CoT prompting of LLMs, and propose a least AoT cached model replacement algorithm for optimizing the provisioning cost. We propose a deep Q-network-based modified second-bid (DQMSB) auction to incentivize network operators, which can enhance allocation efficiency by 23% while guaranteeing strategy-proofness and free from adverse selection. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.05826v2-abstract-full').style.display = 'none'; document.getElementById('2403.05826v2-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a 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