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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.15435">arXiv:2408.15435</a> <span> [<a href="https://arxiv.org/pdf/2408.15435">pdf</a>, <a href="https://arxiv.org/format/2408.15435">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"> Globally Optimal Movable Antenna-Enhanced multi-user Communication: Discrete Antenna Positioning, Motion Power Consumption, and Imperfect CSI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+Y">Yifei Wu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+D">Dongfang Xu</a>, <a href="/search/eess?searchtype=author&query=Ng%2C+D+W+K">Derrick Wing Kwan Ng</a>, <a href="/search/eess?searchtype=author&query=Gerstacker%2C+W">Wolfgang Gerstacker</a>, <a href="/search/eess?searchtype=author&query=Schober%2C+R">Robert Schober</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="2408.15435v1-abstract-short" style="display: inline;"> Movable antennas (MAs) represent a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by dynamically adapting the positions of antenna elements within a designated transmit area. In particular, by employing electro-mechanical MA drivers, the positions of the MA elements can be adjusted to shape a favorable spatial correlation for improving system per… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.15435v1-abstract-full').style.display = 'inline'; document.getElementById('2408.15435v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.15435v1-abstract-full" style="display: none;"> Movable antennas (MAs) represent a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by dynamically adapting the positions of antenna elements within a designated transmit area. In particular, by employing electro-mechanical MA drivers, the positions of the MA elements can be adjusted to shape a favorable spatial correlation for improving system performance. Although preliminary research has explored beamforming designs for MA systems, the intricacies of the power consumption and the precise positioning of MA elements are not well understood. Moreover, the assumption of perfect CSI adopted in the literature is impractical due to the significant pilot overhead and the extensive time to acquire perfect CSI. To address these challenges, we model the motion of MA elements through discrete steps and quantify the associated power consumption as a function of these movements. Furthermore, by leveraging the properties of the MA channel model, we introduce a novel CSI error model tailored for MA systems that facilitates robust resource allocation design. In particular, we optimize the beamforming and the MA positions at the BS to minimize the total BS power consumption, encompassing both radiated and MA motion power while guaranteeing a minimum required SINR for each user. To this end, novel algorithms exploiting the branch and bound (BnB) method are developed to obtain the optimal solution for perfect and imperfect CSI. Moreover, to support practical implementation, we propose low-complexity algorithms with guaranteed convergence by leveraging successive convex approximation (SCA). Our numerical results validate the optimality of the proposed BnB-based algorithms. Furthermore, we unveil that both proposed SCA-based algorithms approach the optimal performance within a few iterations, thus highlighting their practical advantages. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.15435v1-abstract-full').style.display = 'none'; document.getElementById('2408.15435v1-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 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/2407.13591">arXiv:2407.13591</a> <span> [<a href="https://arxiv.org/pdf/2407.13591">pdf</a>, <a href="https://arxiv.org/format/2407.13591">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"> Approximate Partially Decentralized Linear EZF Precoding for Massive MU-MIMO Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kaziu%2C+B">Brikena Kaziu</a>, <a href="/search/eess?searchtype=author&query=Shanin%2C+N">Nikita Shanin</a>, <a href="/search/eess?searchtype=author&query=Spano%2C+D">Danilo Spano</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+L">Li Wang</a>, <a href="/search/eess?searchtype=author&query=Gerstacker%2C+W">Wolfgang Gerstacker</a>, <a href="/search/eess?searchtype=author&query=Schober%2C+R">Robert Schober</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="2407.13591v1-abstract-short" style="display: inline;"> Massive multi-user multiple-input multiple-output (MU-MIMO) systems enable high spatial resolution, high spectral efficiency, and improved link reliability compared to traditional MIMO systems due to the large number of antenna elements deployed at the base station (BS). Nevertheless, conventional massive MU-MIMO BS transceiver designs rely on centralized linear precoding algorithms, which entail… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13591v1-abstract-full').style.display = 'inline'; document.getElementById('2407.13591v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.13591v1-abstract-full" style="display: none;"> Massive multi-user multiple-input multiple-output (MU-MIMO) systems enable high spatial resolution, high spectral efficiency, and improved link reliability compared to traditional MIMO systems due to the large number of antenna elements deployed at the base station (BS). Nevertheless, conventional massive MU-MIMO BS transceiver designs rely on centralized linear precoding algorithms, which entail high interconnect data rates and a prohibitive complexity at the centralized baseband processing unit. In this paper, we consider an MU-MIMO system, where each user device is served with multiple independent data streams in the downlink. To address the aforementioned challenges, we propose a novel decentralized BS architecture, and develop a novel decentralized precoding algorithm based on eigen-zero-forcing (EZF). Our proposed approach relies on parallelizing the baseband processing tasks across multiple antenna clusters at the BS, while minimizing the interconnection requirements between the clusters, and is shown to closely approach the performance of centralized EZF. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13591v1-abstract-full').style.display = 'none'; document.getElementById('2407.13591v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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 VTC2024-Fall</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.17150">arXiv:2405.17150</a> <span> [<a href="https://arxiv.org/pdf/2405.17150">pdf</a>, <a href="https://arxiv.org/ps/2405.17150">ps</a>, <a href="https://arxiv.org/format/2405.17150">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"> Deep Learning-based Joint Channel Prediction and Multibeam Precoding for LEO Satellite Internet of Things </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ying%2C+M">Ming Ying</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Qi%2C+Q">Qiao Qi</a>, <a href="/search/eess?searchtype=author&query=Gerstacker%2C+W">Wolfgang Gerstacker</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="2405.17150v1-abstract-short" style="display: inline;"> Low earth orbit (LEO) satellite internet of things (IoT) is a promising way achieving global Internet of Everything, and thus has been widely recognized as an important component of sixth-generation (6G) wireless networks. Yet, due to high-speed movement of the LEO satellite, it is challenging to acquire timely channel state information (CSI) and design effective multibeam precoding for various Io… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.17150v1-abstract-full').style.display = 'inline'; document.getElementById('2405.17150v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.17150v1-abstract-full" style="display: none;"> Low earth orbit (LEO) satellite internet of things (IoT) is a promising way achieving global Internet of Everything, and thus has been widely recognized as an important component of sixth-generation (6G) wireless networks. Yet, due to high-speed movement of the LEO satellite, it is challenging to acquire timely channel state information (CSI) and design effective multibeam precoding for various IoT applications. To this end, this paper provides a deep learning (DL)-based joint channel prediction and multibeam precoding scheme under adverse environments, e.g., high Doppler shift, long propagation delay, and low satellite payload. {Specifically, this paper first designs a DL-based channel prediction scheme by using convolutional neural networks (CNN) and long short term memory (LSTM), which predicts the CSI of current time slot according to that of previous time slots. With the predicted CSI, this paper designs a DL-based robust multibeam precoding scheme by using a channel augmentation method based on variational auto-encoder (VAE).} Finally, extensive simulation results confirm the effectiveness and robustness of the proposed scheme in LEO satellite IoT. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.17150v1-abstract-full').style.display = 'none'; document.getElementById('2405.17150v1-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">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">IEEE Transactions on Wireless Communications, 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/2402.04728">arXiv:2402.04728</a> <span> [<a href="https://arxiv.org/pdf/2402.04728">pdf</a>, <a href="https://arxiv.org/format/2402.04728">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"> Detection Schemes with Low-Resolution ADCs and Spatial Oversampling for Transmission with Higher-Order Constellations in the Terahertz Band </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Forsch%2C+C">Christian Forsch</a>, <a href="/search/eess?searchtype=author&query=Zillmann%2C+P">Peter Zillmann</a>, <a href="/search/eess?searchtype=author&query=Alrabadi%2C+O">Osama Alrabadi</a>, <a href="/search/eess?searchtype=author&query=Brueck%2C+S">Stefan Brueck</a>, <a href="/search/eess?searchtype=author&query=Gerstacker%2C+W">Wolfgang Gerstacker</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.04728v1-abstract-short" style="display: inline;"> In this work, we consider Terahertz (THz) communications with low-resolution uniform quantization and spatial oversampling at the receiver side. We compare different analog-to-digital converter (ADC) parametrizations in a fair manner by keeping the ADC power consumption constant. Here, 1-, 2-, and 3-bit quantization is investigated with different oversampling factors. We analytically compute the s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.04728v1-abstract-full').style.display = 'inline'; document.getElementById('2402.04728v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.04728v1-abstract-full" style="display: none;"> In this work, we consider Terahertz (THz) communications with low-resolution uniform quantization and spatial oversampling at the receiver side. We compare different analog-to-digital converter (ADC) parametrizations in a fair manner by keeping the ADC power consumption constant. Here, 1-, 2-, and 3-bit quantization is investigated with different oversampling factors. We analytically compute the statistics of the detection variable, and we propose the optimal as well as several suboptimal detection schemes for arbitrary quantization resolutions. Then, we evaluate the symbol error rate (SER) of the different detectors for a 16- and a 64-ary quadrature amplitude modulation (QAM) constellation. The results indicate that there is a noticeable performance degradation of the suboptimal detection schemes compared to the optimal scheme when the constellation size is larger than the number of quantization levels. Furthermore, at low signal-to-noise ratios (SNRs), 1-bit quantization outperforms 2- and 3-bit quantization, respectively, even when employing higher-order constellations. We confirm our analytical results by Monte Carlo simulations. Both a pure line-of-sight (LoS) and a more realistically modeled indoor THz channel are considered. Then, we optimize the input signal constellation with respect to SER for 1-bit quantization. The results show that the minimum SER can be lowered significantly for 16-QAM by increasing the distance between the inner and outer points of the input constellation. For larger constellations, however, the achievable reduction of the minimum SER is much smaller compared to 16-QAM. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.04728v1-abstract-full').style.display = 'none'; document.getElementById('2402.04728v1-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 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 19 figures, submitted for possible journal 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/2312.02343">arXiv:2312.02343</a> <span> [<a href="https://arxiv.org/pdf/2312.02343">pdf</a>, <a href="https://arxiv.org/format/2312.02343">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"> Time-based vs. Fingerprinting-based Positioning Using Artificial Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kirmaz%2C+A">Anil Kirmaz</a>, <a href="/search/eess?searchtype=author&query=Sahin%2C+T">Taylan Sahin</a>, <a href="/search/eess?searchtype=author&query=Michalopoulos%2C+D+S">Diomidis S. Michalopoulos</a>, <a href="/search/eess?searchtype=author&query=Gerstacker%2C+W">Wolfgang Gerstacker</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="2312.02343v1-abstract-short" style="display: inline;"> High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.02343v1-abstract-full').style.display = 'inline'; document.getElementById('2312.02343v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.02343v1-abstract-full" style="display: none;"> High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine learning (ML)-based estimators have been proposed to overcome these challenges. RF measurements can be utilized for positioning in multiple ways resulting in time-based, angle-based and fingerprinting-based methods. Different methods, however, impose different implementation requirements to the system, and may perform differently in terms of accuracy for a given setting. In this paper, we use artificial neural networks (ANNs) to realize time-of-arrival (ToA)-based and channel impulse response (CIR) fingerprinting-based positioning. We compare their performance for different indoor environments based on real-world ultra-wideband (UWB) measurements. We first show that using ML techniques helps to improve the estimation accuracy compared to conventional techniques for time-based positioning. When comparing time-based and fingerprinting schemes using ANNs, we show that the favorable method in terms of positioning accuracy is different for different environments, where the accuracy is affected not only by the radio propagation conditions but also the density and distribution of reference user locations used for fingerprinting. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.02343v1-abstract-full').style.display = 'none'; document.getElementById('2312.02343v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for presentation at International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.02304">arXiv:2308.02304</a> <span> [<a href="https://arxiv.org/pdf/2308.02304">pdf</a>, <a href="https://arxiv.org/format/2308.02304">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"> Movable Antenna-Enhanced Multiuser Communication: Optimal Discrete Antenna Positioning and Beamforming </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+Y">Yifei Wu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+D">Dongfang Xu</a>, <a href="/search/eess?searchtype=author&query=Ng%2C+D+W+K">Derrick Wing Kwan Ng</a>, <a href="/search/eess?searchtype=author&query=Gerstacker%2C+W">Wolfgang Gerstacker</a>, <a href="/search/eess?searchtype=author&query=Schober%2C+R">Robert Schober</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2308.02304v1-abstract-short" style="display: inline;"> Movable antennas (MAs) are a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by flexibly adapting the positions of the antenna elements within a given transmit area. In this paper, we model the motion of the MA elements as discrete movements and study the corresponding resource allocation problem for MA-enabled multiuser multiple-input single-outp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.02304v1-abstract-full').style.display = 'inline'; document.getElementById('2308.02304v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.02304v1-abstract-full" style="display: none;"> Movable antennas (MAs) are a promising paradigm to enhance the spatial degrees of freedom of conventional multi-antenna systems by flexibly adapting the positions of the antenna elements within a given transmit area. In this paper, we model the motion of the MA elements as discrete movements and study the corresponding resource allocation problem for MA-enabled multiuser multiple-input single-output (MISO) communication systems. Specifically, we jointly optimize the beamforming and the MA positions at the base station (BS) for the minimization of the total transmit power while guaranteeing the minimum required signal-to-interference-plus-noise ratio (SINR) of each individual user. To obtain the globally optimal solution to the formulated resource allocation problem, we develop an iterative algorithm capitalizing on the generalized Bender's decomposition with guaranteed convergence. Our numerical results demonstrate that the proposed MA-enabled communication system can significantly reduce the BS transmit power and the number of antenna elements needed to achieve a desired performance compared to state-of-the-art techniques, such as antenna selection. Furthermore, we observe that refining the step size of the MA motion driver improves performance at the expense of a higher computational complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.02304v1-abstract-full').style.display = 'none'; document.getElementById('2308.02304v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.04510">arXiv:2301.04510</a> <span> [<a href="https://arxiv.org/pdf/2301.04510">pdf</a>, <a href="https://arxiv.org/format/2301.04510">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"> Time of Arrival Error Estimation for Positioning Using Convolutional Neural Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kirmaz%2C+A">Anil Kirmaz</a>, <a href="/search/eess?searchtype=author&query=%C5%9Eahin%2C+T">Taylan 艦ahin</a>, <a href="/search/eess?searchtype=author&query=Michalopoulos%2C+D+S">Diomidis S. Michalopoulos</a>, <a href="/search/eess?searchtype=author&query=Ashraf%2C+M+I">Muhammad Ikram Ashraf</a>, <a href="/search/eess?searchtype=author&query=Gerstacker%2C+W">Wolfgang Gerstacker</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2301.04510v1-abstract-short" style="display: inline;"> Wireless high-accuracy positioning has recently attracted growing research interest due to diversified nature of applications such as industrial asset tracking, autonomous driving, process automation, and many more. However, obtaining a highly accurate location information is hampered by challenges due to the radio environment. A major source of error for time-based positioning methods is inaccura… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04510v1-abstract-full').style.display = 'inline'; document.getElementById('2301.04510v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.04510v1-abstract-full" style="display: none;"> Wireless high-accuracy positioning has recently attracted growing research interest due to diversified nature of applications such as industrial asset tracking, autonomous driving, process automation, and many more. However, obtaining a highly accurate location information is hampered by challenges due to the radio environment. A major source of error for time-based positioning methods is inaccurate time-of-arrival (ToA) or range estimation. Existing machine learning-based solutions to mitigate such errors rely on propagation environment classification hindered by a low number of classes, employ a set of features representing channel measurements only to a limited extent, or account for only device-specific proprietary methods of ToA estimation. In this paper, we propose convolutional neural networks (CNNs) to estimate and mitigate the errors of a variety of ToA estimation methods utilizing channel impulse responses (CIRs). Based on real-world measurements from two independent campaigns, the proposed method yields significant improvements in ranging accuracy (up to 37%) of the state-of-the-art ToA estimators, often eliminating the need of optimizing the underlying conventional methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.04510v1-abstract-full').style.display = 'none'; document.getElementById('2301.04510v1-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 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for presentation at IEEE WCNC 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.05072">arXiv:2009.05072</a> <span> [<a href="https://arxiv.org/pdf/2009.05072">pdf</a>, <a href="https://arxiv.org/ps/2009.05072">ps</a>, <a href="https://arxiv.org/format/2009.05072">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"> Efficient Detectors for Telegram Splitting based Transmission in Low Power Wide Area Networks with Bursty Interference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kisseleff%2C+S">Steven Kisseleff</a>, <a href="/search/eess?searchtype=author&query=Kneissl%2C+J">Jakob Kneissl</a>, <a href="/search/eess?searchtype=author&query=Kilian%2C+G">Gerd Kilian</a>, <a href="/search/eess?searchtype=author&query=Gerstacker%2C+W+H">Wolfgang H. Gerstacker</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="2009.05072v1-abstract-short" style="display: inline;"> Low Power Wide Area (LPWA) networks are known to be highly vulnerable to external in-band interference in terms of packet collisions which may substantially degrade the system performance. In order to enhance the performance in such cases, the telegram splitting (TS) method has been proposed recently. This approach exploits the typical burstiness of the interference via forward error correction (F… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.05072v1-abstract-full').style.display = 'inline'; document.getElementById('2009.05072v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.05072v1-abstract-full" style="display: none;"> Low Power Wide Area (LPWA) networks are known to be highly vulnerable to external in-band interference in terms of packet collisions which may substantially degrade the system performance. In order to enhance the performance in such cases, the telegram splitting (TS) method has been proposed recently. This approach exploits the typical burstiness of the interference via forward error correction (FEC) and offers a substantial performance improvement compared to other methods for packet transmissions in LPWA networks. While it has been already demonstrated that the TS method benefits from knowledge on the current interference state at the receiver side, corresponding practical receiver algorithms of high performance are still missing. The modeling of the bursty interference via Markov chains leads to the optimal detector in terms of a-posteriori symbol error probability. However, this solution requires a high computational complexity, assumes an a-priori knowledge on the interference characteristics and lacks flexibility. We propose a further developed scheme with increased flexibility and introduce an approach to reduce its complexity while maintaining a close-to-optimum performance. In particular, the proposed low complexity solution substantially outperforms existing practical methods in terms of packet error rate and therefore is highly beneficial for practical LPWA network scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.05072v1-abstract-full').style.display = 'none'; document.getElementById('2009.05072v1-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 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </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 for publication in IEEE Transactions 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/1907.05126">arXiv:1907.05126</a> <span> [<a href="https://arxiv.org/pdf/1907.05126">pdf</a>, <a href="https://arxiv.org/format/1907.05126">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"> Approximate Message Passing for Indoor THz Channel Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Schram%2C+V">Viktoria Schram</a>, <a href="/search/eess?searchtype=author&query=Bereyhi%2C+A">Ali Bereyhi</a>, <a href="/search/eess?searchtype=author&query=Zaech%2C+J">Jan-Nico Zaech</a>, <a href="/search/eess?searchtype=author&query=M%C3%BCller%2C+R+R">Ralf R. M眉ller</a>, <a href="/search/eess?searchtype=author&query=Gerstacker%2C+W+H">Wolfgang H. Gerstacker</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="1907.05126v1-abstract-short" style="display: inline;"> Compressed sensing (CS) deals with the problem of reconstructing a sparse vector from an under-determined set of observations. Approximate message passing (AMP) is a technique used in CS based on iterative thresholding and inspired by belief propagation in graphical models. Due to the high transmission rate and a high molecular absorption, spreading loss and reflection loss, the discrete-time chan… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.05126v1-abstract-full').style.display = 'inline'; document.getElementById('1907.05126v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1907.05126v1-abstract-full" style="display: none;"> Compressed sensing (CS) deals with the problem of reconstructing a sparse vector from an under-determined set of observations. Approximate message passing (AMP) is a technique used in CS based on iterative thresholding and inspired by belief propagation in graphical models. Due to the high transmission rate and a high molecular absorption, spreading loss and reflection loss, the discrete-time channel impulse response (CIR) of a typical indoor THz channel is very long and exhibits an approximately sparse characteristic. In this paper, we develop AMP based channel estimation algorithms for indoor THz communications. The performance of these algorithms is compared to the state of the art. We apply AMP with soft- and hard-thresholding. Unlike the common applications in which AMP with hard-thresholding diverges, the properties of the THz channel favor this approach. It is shown that THz channel estimation via hard-thresholding AMP outperforms all previously proposed methods and approaches the oracle based performance closely. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1907.05126v1-abstract-full').style.display = 'none'; document.getElementById('1907.05126v1-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, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2019. </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,7 figures, conference: presented at BalkanCom 2019</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search 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