Energy harvesting is a promising technique to address the energy hunger problem for thousands of wireless devices. In Radio Frequency (RF) energy harvesting systems, a wireless device first harvests energy and then transmits data with this energy, hence the ‘harvest-then-transmit’ (HTT) principle is widely adopted. We must carefully design the HTT schedule, i.e., schedule the timing between harvesting and transmission, and decide the data transmission power such that the throughput can be maximized with the limited harvested energy. Distinct from existing work, we assume energy harvested from RF sources is time-varying, which is more practical but more difficult to handle. We first discover a surprising result that the optimal transmission power is independent of the transmission time, but solely depends on the RF harvesting power, for a simple case when the energy harvesting is stable. We then obtain an optimal offline HTT-scheduling for the general case that allows the RF harvesting power to vary with time. To the best of our knowledge, it is the first optimal HTT-scheduling algorithm that achieves maximum data throughput for time-varying RF powered systems. Finally, an efficient online heuristic algorithm is designed based on the offline optimality properties. Simulations show that the proposed online algorithm has superior performance, which achieves more than 90% of the offline maximum throughput in most cases.
{"title":"Optimal Harvest-Then-Transmit Scheduling for Throughput Maximization in Time-Varying RF Powered Systems","authors":"Feng Shan;Junzhou Luo;Qiao Jin;Liwen Cao;Weiwei Wu;Zhen Ling;Fang Dong","doi":"10.1109/JSAC.2024.3431569","DOIUrl":"10.1109/JSAC.2024.3431569","url":null,"abstract":"Energy harvesting is a promising technique to address the energy hunger problem for thousands of wireless devices. In Radio Frequency (RF) energy harvesting systems, a wireless device first harvests energy and then transmits data with this energy, hence the ‘harvest-then-transmit’ (HTT) principle is widely adopted. We must carefully design the HTT schedule, i.e., schedule the timing between harvesting and transmission, and decide the data transmission power such that the throughput can be maximized with the limited harvested energy. Distinct from existing work, we assume energy harvested from RF sources is time-varying, which is more practical but more difficult to handle. We first discover a surprising result that the optimal transmission power is independent of the transmission time, but solely depends on the RF harvesting power, for a simple case when the energy harvesting is stable. We then obtain an optimal offline HTT-scheduling for the general case that allows the RF harvesting power to vary with time. To the best of our knowledge, it is the first optimal HTT-scheduling algorithm that achieves maximum data throughput for time-varying RF powered systems. Finally, an efficient online heuristic algorithm is designed based on the offline optimality properties. Simulations show that the proposed online algorithm has superior performance, which achieves more than 90% of the offline maximum throughput in most cases.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 11","pages":"3140-3156"},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142101415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Significant challenges remain for realizing precise positioning and velocity estimation in practical perceptive vehicular networks (PVN) that rely on the emerging integrated sensing and communication (ISAC) technology. Firstly, complicated wireless propagation environment generates undesired clutter, which degrades the vehicular sensing performance and increases the computational complexity. Secondly, in practical PVN, multiple types of parameters individually estimated are not well associated with specific vehicles, which may cause error propagation in multiple-vehicle positioning. Thirdly, radio transceivers in a PVN are naturally asynchronous, which causes strong range and velocity ambiguity in vehicular sensing. To overcome these challenges, in this paper 1) we introduce a moving target indication (MTI) based joint clutter suppression and sensing algorithm, and analyze its clutter-suppression performance and the Cramér-Rao lower bound (CRLB) of the paired range-velocity estimation upon using the proposed clutter suppression algorithm; 2) we design an algorithm (and its low-complexity versions) for associating individual direction-of-arrival (DOA) estimates with the paired range-velocity estimates based on “domain transformation”; 3) we propose the first viable carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive vehicular sensing in non-line-of-sight (NLOS) environments. This algorithm treats the delay-Doppler spectrum of the signals reflected by static objects as an environment-specific “fingerprint spectrum”, which is shown to exhibit a circular shift property upon changing the CFO and/or TO. Then, the CFO and TO are efficiently estimated by acquiring the number of circular shifts, and we also analyse the mean squared error (MSE) performance of the proposed time-frequency synchronization algorithm. Finally, simulation results demonstrate the performance advantages of our algorithms under diverse configurations, while corroborating the theoretical analysis.
{"title":"Clutter Suppression, Time-Frequency Synchronization, and Sensing Parameter Association in Asynchronous Perceptive Vehicular Networks","authors":"Xiao-Yang Wang;Shaoshi Yang;Jianhua Zhang;Christos Masouros;Ping Zhang","doi":"10.1109/JSAC.2024.3414581","DOIUrl":"10.1109/JSAC.2024.3414581","url":null,"abstract":"Significant challenges remain for realizing precise positioning and velocity estimation in practical perceptive vehicular networks (PVN) that rely on the emerging integrated sensing and communication (ISAC) technology. Firstly, complicated wireless propagation environment generates undesired clutter, which degrades the vehicular sensing performance and increases the computational complexity. Secondly, in practical PVN, multiple types of parameters individually estimated are not well associated with specific vehicles, which may cause error propagation in multiple-vehicle positioning. Thirdly, radio transceivers in a PVN are naturally asynchronous, which causes strong range and velocity ambiguity in vehicular sensing. To overcome these challenges, in this paper 1) we introduce a moving target indication (MTI) based joint clutter suppression and sensing algorithm, and analyze its clutter-suppression performance and the Cramér-Rao lower bound (CRLB) of the paired range-velocity estimation upon using the proposed clutter suppression algorithm; 2) we design an algorithm (and its low-complexity versions) for associating individual direction-of-arrival (DOA) estimates with the paired range-velocity estimates based on “domain transformation”; 3) we propose the first viable carrier frequency offset (CFO) and time offset (TO) estimation algorithm that supports passive vehicular sensing in non-line-of-sight (NLOS) environments. This algorithm treats the delay-Doppler spectrum of the signals reflected by static objects as an environment-specific “fingerprint spectrum”, which is shown to exhibit a circular shift property upon changing the CFO and/or TO. Then, the CFO and TO are efficiently estimated by acquiring the number of circular shifts, and we also analyse the mean squared error (MSE) performance of the proposed time-frequency synchronization algorithm. Finally, simulation results demonstrate the performance advantages of our algorithms under diverse configurations, while corroborating the theoretical analysis.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2719-2736"},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/JSAC.2024.3431572
Li Qiao;Zhen Gao;Mahdi Boloursaz Mashhadi;Deniz Gündüz
Over-the-air computation (AirComp) is a promising technology converging communication and computation over wireless networks, which can be particularly effective in model training, inference, and more emerging edge intelligence applications. AirComp relies on uncoded transmission of individual signals, which are added naturally over the multiple access channel thanks to the superposition property of the wireless medium. Despite significantly improved communication efficiency, how to accommodate AirComp in the existing and future digital communication networks, that are based on discrete modulation schemes, remains a challenge. This paper proposes a massive digital AirComp (MD-AirComp) scheme, that leverages an unsourced massive access protocol, to enhance compatibility with both current and next-generation wireless networks. MD-AirComp utilizes vector quantization to reduce the uplink communication overhead, and employs shared quantization and modulation codebooks. At the receiver, we propose a near-optimal approximate message passing-based algorithm to compute the model aggregation results from the superposed sequences, which relies on estimating the number of devices transmitting each code sequence, rather than trying to decode the messages of individual transmitters. We apply MD-AirComp to federated edge learning (FEEL), and show that it significantly accelerates FEEL convergence compared to state-of-the-art while using the same amount of communication resources.
{"title":"Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learning","authors":"Li Qiao;Zhen Gao;Mahdi Boloursaz Mashhadi;Deniz Gündüz","doi":"10.1109/JSAC.2024.3431572","DOIUrl":"10.1109/JSAC.2024.3431572","url":null,"abstract":"Over-the-air computation (AirComp) is a promising technology converging communication and computation over wireless networks, which can be particularly effective in model training, inference, and more emerging edge intelligence applications. AirComp relies on uncoded transmission of individual signals, which are added naturally over the multiple access channel thanks to the superposition property of the wireless medium. Despite significantly improved communication efficiency, how to accommodate AirComp in the existing and future digital communication networks, that are based on discrete modulation schemes, remains a challenge. This paper proposes a massive digital AirComp (MD-AirComp) scheme, that leverages an unsourced massive access protocol, to enhance compatibility with both current and next-generation wireless networks. MD-AirComp utilizes vector quantization to reduce the uplink communication overhead, and employs shared quantization and modulation codebooks. At the receiver, we propose a near-optimal approximate message passing-based algorithm to compute the model aggregation results from the superposed sequences, which relies on estimating the number of devices transmitting each code sequence, rather than trying to decode the messages of individual transmitters. We apply MD-AirComp to federated edge learning (FEEL), and show that it significantly accelerates FEEL convergence compared to state-of-the-art while using the same amount of communication resources.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 11","pages":"3078-3094"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142085043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-20DOI: 10.1109/JSAC.2024.3439089
{"title":"TechRxiv: Share Your Preprint Research With the World!","authors":"","doi":"10.1109/JSAC.2024.3439089","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3439089","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 9","pages":"2601-2601"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640317","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-20DOI: 10.1109/JSAC.2024.3437273
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/JSAC.2024.3437273","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3437273","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 9","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640346","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-20DOI: 10.1109/JSAC.2024.3423608
Yang Yang;Mingzhe Chen;Yufei Blankenship;Jemin Lee;Zabih Ghassemlooy;Julian Cheng;Shiwen Mao
Positioning and sensing have long been an important area of research. Recently, this field has attracted more attention due to the rapid deployment of emerging applications and next-generation communication networks. On the one hand, emerging applications like extended reality (XR) and autonomous vehicle systems need to precisely “see” the physical world, thus greatly increasing the demands on positioning and sensing technologies. Moreover, these applications also require data rate communication links, and thus technologies like cellular networks and WiFi are excellent for supporting these applications. On the other hand, with the evolution of wireless networks, positioning, and sensing have also been considered important functions of future wireless networks that can further enhance communication performance. Although existing wireless communication has achieved significant success in the past several decades, achieving satisfying positioning and sensing performance for these emerging applications remains a challenge due to the complexity of the wireless environment and the stringent performance requirements.
{"title":"Guest Editorial Positioning and Sensing Over Wireless Networks—Part I","authors":"Yang Yang;Mingzhe Chen;Yufei Blankenship;Jemin Lee;Zabih Ghassemlooy;Julian Cheng;Shiwen Mao","doi":"10.1109/JSAC.2024.3423608","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3423608","url":null,"abstract":"Positioning and sensing have long been an important area of research. Recently, this field has attracted more attention due to the rapid deployment of emerging applications and next-generation communication networks. On the one hand, emerging applications like extended reality (XR) and autonomous vehicle systems need to precisely “see” the physical world, thus greatly increasing the demands on positioning and sensing technologies. Moreover, these applications also require data rate communication links, and thus technologies like cellular networks and WiFi are excellent for supporting these applications. On the other hand, with the evolution of wireless networks, positioning, and sensing have also been considered important functions of future wireless networks that can further enhance communication performance. Although existing wireless communication has achieved significant success in the past several decades, achieving satisfying positioning and sensing performance for these emerging applications remains a challenge due to the complexity of the wireless environment and the stringent performance requirements.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 9","pages":"2143-2148"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-20DOI: 10.1109/JSAC.2024.3437271
{"title":"IEEE Journal on Selected Areas in Communications Publication Information","authors":"","doi":"10.1109/JSAC.2024.3437271","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3437271","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 9","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640263","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-20DOI: 10.1109/JSAC.2024.3439091
{"title":"IEEE Open Access Publishing","authors":"","doi":"10.1109/JSAC.2024.3439091","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3439091","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 9","pages":"2602-2602"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-19DOI: 10.1109/JSAC.2024.3413950
Bernardo Camajori Tedeschini;Girim Kwon;Monica Nicoli;Moe Z. Win
In the evolving landscape of 5G new radio and related 6G evolution, achieving centimeter-level dynamic positioning is pivotal, especially in cooperative intelligent transportation system frameworks. With the challenges posed by higher path loss and blockages in the new frequency bands (i.e., millimeter waves), machine learning (ML) offers new approaches to draw location information from space-time wide-bandwidth radio signals and enable enhanced location-based services. This paper presents an approach to real-time 6G location tracking in urban settings with frequent signal blockages. We introduce a novel teacher-student Bayesian neural network (BNN) method, called Bayesian bright knowledge (BBK), that predicts both the location estimate and the associated uncertainty in real-time. Moreover, we propose a seamless integration of BNNs into a cellular multi-base station tracking system, where more complex channel measurements are taken into account. Our method employs a deep learning (DL)-based autoencoder structure that leverages the complete channel impulse response to deduce location-specific attributes in both line-of-sight and non-line-of-sight environments. Testing in 3GPP specification-compliant urban micro (UMi) scenario with ray-tracing and traffic simulations confirms the BBK’s superiority in estimating uncertainties and handling out-of-distribution testing positions. In dynamic conditions, our BNN-based tracking system surpasses geometric-based tracking techniques and state-of-the-art DL models, localizing a moving target with a median error of 46 cm.
{"title":"Real-Time Bayesian Neural Networks for 6G Cooperative Positioning and Tracking","authors":"Bernardo Camajori Tedeschini;Girim Kwon;Monica Nicoli;Moe Z. Win","doi":"10.1109/JSAC.2024.3413950","DOIUrl":"10.1109/JSAC.2024.3413950","url":null,"abstract":"In the evolving landscape of 5G new radio and related 6G evolution, achieving centimeter-level dynamic positioning is pivotal, especially in cooperative intelligent transportation system frameworks. With the challenges posed by higher path loss and blockages in the new frequency bands (i.e., millimeter waves), machine learning (ML) offers new approaches to draw location information from space-time wide-bandwidth radio signals and enable enhanced location-based services. This paper presents an approach to real-time 6G location tracking in urban settings with frequent signal blockages. We introduce a novel teacher-student Bayesian neural network (BNN) method, called Bayesian bright knowledge (BBK), that predicts both the location estimate and the associated uncertainty in real-time. Moreover, we propose a seamless integration of BNNs into a cellular multi-base station tracking system, where more complex channel measurements are taken into account. Our method employs a deep learning (DL)-based autoencoder structure that leverages the complete channel impulse response to deduce location-specific attributes in both line-of-sight and non-line-of-sight environments. Testing in 3GPP specification-compliant urban micro (UMi) scenario with ray-tracing and traffic simulations confirms the BBK’s superiority in estimating uncertainties and handling out-of-distribution testing positions. In dynamic conditions, our BNN-based tracking system surpasses geometric-based tracking techniques and state-of-the-art DL models, localizing a moving target with a median error of 46 cm.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 9","pages":"2322-2338"},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-19DOI: 10.1109/JSAC.2024.3431573
Kaiyue Hou;Shuowen Zhang
In this paper, we study a secure integrated sensing and communication (ISAC) system where one multi-antenna base station (BS) simultaneously communicates with one single-antenna user and senses the location parameter of a target serving as a potential eavesdropper via its reflected echo signals. In particular, we consider a challenging scenario where the target’s location is unknown and random, while its distribution information is known a priori based on empirical data or target movement pattern. First, we derive the posterior Cramér-Rao bound (PCRB) of the mean-squared error (MSE) in target location sensing, which has a complicated expression. To draw more insights, we derive a tight approximation of the PCRB in closed form, which indicates that the transmit beamforming should achieve a “probability-dependent power focusing” effect over possible target locations. Next, considering an artificial noise (AN) based beamforming structure at the BS to alleviate information eavesdropping and enhance the target’s reflected signal power for sensing, we formulate the transmit beamforming optimization problem to maximize the worst-case secrecy rate among all possible target (eavesdropper) locations, subject to a maximum threshold on the sensing PCRB. The formulated problem is non-convex and difficult to solve. To deal with this problem, we first show that the problem can be solved via a two-stage method, by first obtaining the optimal beamforming corresponding to any given threshold on the signal-to-interference-plus-noise ratio (SINR) at the eavesdropper, and then obtaining the optimal threshold and consequently the optimal beamforming via one-dimensional search of the threshold. By applying the Charnes-Cooper equivalent transformation and semi-definite relaxation (SDR), we relax the first problem into a convex form and further prove that the rank-one relaxation is tight, based on which the optimal solution of the original beamforming optimization problem can be obtained via the two-stage method with polynomial-time complexity. Then, we further propose two suboptimal solutions with lower complexity by designing the information beam and/or AN beams in the null spaces of the possible eavesdropper channels and/or the user channel, respectively. Numerical results validate the effectiveness of our designs in achieving secure communication and high-quality sensing in the challenging scenario with unknown target (eavesdropper) location.
{"title":"Optimal Beamforming for Secure Integrated Sensing and Communication Exploiting Target Location Distribution","authors":"Kaiyue Hou;Shuowen Zhang","doi":"10.1109/JSAC.2024.3431573","DOIUrl":"10.1109/JSAC.2024.3431573","url":null,"abstract":"In this paper, we study a secure integrated sensing and communication (ISAC) system where one multi-antenna base station (BS) simultaneously communicates with one single-antenna user and senses the location parameter of a target serving as a potential eavesdropper via its reflected echo signals. In particular, we consider a challenging scenario where the target’s location is unknown and random, while its distribution information is known a priori based on empirical data or target movement pattern. First, we derive the posterior Cramér-Rao bound (PCRB) of the mean-squared error (MSE) in target location sensing, which has a complicated expression. To draw more insights, we derive a tight approximation of the PCRB in closed form, which indicates that the transmit beamforming should achieve a “probability-dependent power focusing” effect over possible target locations. Next, considering an artificial noise (AN) based beamforming structure at the BS to alleviate information eavesdropping and enhance the target’s reflected signal power for sensing, we formulate the transmit beamforming optimization problem to maximize the worst-case secrecy rate among all possible target (eavesdropper) locations, subject to a maximum threshold on the sensing PCRB. The formulated problem is non-convex and difficult to solve. To deal with this problem, we first show that the problem can be solved via a two-stage method, by first obtaining the optimal beamforming corresponding to any given threshold on the signal-to-interference-plus-noise ratio (SINR) at the eavesdropper, and then obtaining the optimal threshold and consequently the optimal beamforming via one-dimensional search of the threshold. By applying the Charnes-Cooper equivalent transformation and semi-definite relaxation (SDR), we relax the first problem into a convex form and further prove that the rank-one relaxation is tight, based on which the optimal solution of the original beamforming optimization problem can be obtained via the two-stage method with polynomial-time complexity. Then, we further propose two suboptimal solutions with lower complexity by designing the information beam and/or AN beams in the null spaces of the possible eavesdropper channels and/or the user channel, respectively. Numerical results validate the effectiveness of our designs in achieving secure communication and high-quality sensing in the challenging scenario with unknown target (eavesdropper) location.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 11","pages":"3125-3139"},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142007574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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