In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in 5G vehicular networks through a comprehensive end-to-end methodology encompassing ray-tracing-based channel modeling, novel theoretical performance bounds, high-resolution channel parameter estimation, and geometric positioning using a round-trip-time (RTT) protocol. We first derive a novel, approximate Cramér-Rao bound (CRB) on the connected road user (CRU) position, explicitly taking into account multipath interference, path merging, and the RTT protocol. Capitalizing on tensor decomposition and ESPRIT methods, we propose high-resolution channel parameter estimation algorithms specifically tailored to dense multipath V2X sidelink environments, designed to detect multipath components (MPCs) and extract line-of-sight (LoS) parameters. Finally, using realistic ray-tracing data and antenna patterns, comprehensive simulations are conducted to evaluate channel estimation and positioning performance, indicating that sub-meter accuracy can be achieved in sub-6 GHz V2X with the proposed algorithms.
{"title":"V2X Sidelink Positioning in FR1: Scenarios, Algorithms, and Performance Evaluation","authors":"Yu Ge;Maximilian Stark;Musa Furkan Keskin;Frank Hofmann;Thomas Hansen;Henk Wymeersch","doi":"10.1109/JSAC.2024.3414579","DOIUrl":"10.1109/JSAC.2024.3414579","url":null,"abstract":"In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in 5G vehicular networks through a comprehensive end-to-end methodology encompassing ray-tracing-based channel modeling, novel theoretical performance bounds, high-resolution channel parameter estimation, and geometric positioning using a round-trip-time (RTT) protocol. We first derive a novel, approximate Cramér-Rao bound (CRB) on the connected road user (CRU) position, explicitly taking into account multipath interference, path merging, and the RTT protocol. Capitalizing on tensor decomposition and ESPRIT methods, we propose high-resolution channel parameter estimation algorithms specifically tailored to dense multipath V2X sidelink environments, designed to detect multipath components (MPCs) and extract line-of-sight (LoS) parameters. Finally, using realistic ray-tracing data and antenna patterns, comprehensive simulations are conducted to evaluate channel estimation and positioning performance, indicating that sub-meter accuracy can be achieved in sub-6 GHz V2X with the proposed algorithms.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933188","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 : 2024-06-14DOI: 10.1109/JSAC.2024.3414582
Tuo Wu;Cunhua Pan;Kangda Zhi;Hong Ren;Maged Elkashlan;Cheng-Xiang Wang;Robert Schober;Xiao-Hu You
This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In Stage I, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in Stage II, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).
{"title":"Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?","authors":"Tuo Wu;Cunhua Pan;Kangda Zhi;Hong Ren;Maged Elkashlan;Cheng-Xiang Wang;Robert Schober;Xiao-Hu You","doi":"10.1109/JSAC.2024.3414582","DOIUrl":"10.1109/JSAC.2024.3414582","url":null,"abstract":"This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In Stage I, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in Stage II, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968805","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3413955
Yinghui He;Jianwei Liu;Mo Li;Guanding Yu;Jinsong Han
Given the fact that WiFi-based sensing can be realized through the reuse of WiFi communication facilities and frequency bands, integrated sensing and communication (ISAC) emerges as a pivotal direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from exclusive WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it demands high-quality and sufficient CSI measurements. In this paper, we propose SenCom as a step towards forward-compatible ISAC solution. SenCom extracts CSI from general WiFi packets, enabling CSI calibration across different WiFi communication modes and delivering quality CSI measurements for upper-layer sensing applications. A fitting-resampling scheme and an incentive strategy are also developed. The former one is to obtain evenly sampled CSI with consistent dimensionality and the latter one is to guarantee sufficient CSI measurements over time. We build a prototype of SenCom and conduct extensive experiments involving 15 participants. The results show that SenCom’s competence for a variety of sensing tasks while making minimal compromises to WiFi communication performance.
{"title":"Forward-Compatible Integrated Sensing and Communication for WiFi","authors":"Yinghui He;Jianwei Liu;Mo Li;Guanding Yu;Jinsong Han","doi":"10.1109/JSAC.2024.3413955","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413955","url":null,"abstract":"Given the fact that WiFi-based sensing can be realized through the reuse of WiFi communication facilities and frequency bands, integrated sensing and communication (ISAC) emerges as a pivotal direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from exclusive WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it demands high-quality and sufficient CSI measurements. In this paper, we propose SenCom as a step towards forward-compatible ISAC solution. SenCom extracts CSI from general WiFi packets, enabling CSI calibration across different WiFi communication modes and delivering quality CSI measurements for upper-layer sensing applications. A fitting-resampling scheme and an incentive strategy are also developed. The former one is to obtain evenly sampled CSI with consistent dimensionality and the latter one is to guarantee sufficient CSI measurements over time. We build a prototype of SenCom and conduct extensive experiments involving 15 participants. The results show that SenCom’s competence for a variety of sensing tasks while making minimal compromises to WiFi communication performance.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013418","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3413998
Sharbel Kozhaya;Zaher M. Kassas
The fundamental tracking performance in radio navigation is characterized, leading to optimal receiver design considerations. First, a generalized beacon model is proposed and its sufficient, salient parameters are defined. Second, closed-form approximations of the delay, Doppler, Doppler stretch, and Doppler rate ambiguity functions (AFs) and a generalized coherent integration efficiency model are proposed. Third, design considerations and optimal coherent processing interval (CPI) length selection are presented, based on the complete navigation framework, entailing the: (i) beacon’s parameters, (ii) channel dynamics between the transmitter and receiver, (iii) employed clocks by the transmitter and receiver, (iv) search space adopted in the acquisition stage, and (v) dynamical model’s order employed in the tracking stage. Fourth, the acquisition and tracking stages of the navigation receiver architecture are discussed. Fifth, three sets of experimental results are presented validating the proposed closed-form approximation of the Doppler stretch and Doppler rate AFs and demonstrating the performance of a receiver tuned by the proposed design considerations in acquiring, tracking, and localization, namely: (i) aircraft tracking of terrestrial 4G signals, (ii) stationary receiver tracking of Starlink low Earth orbit (LEO) signals, and (iii) stationary receiver localization with Starlink and OneWeb LEO signals. For the Starlink and OneWeb receiver localization experiment, the receiver was capable of tracking the Doppler and carrier phase of 5 Starlink and 3 OneWeb LEO satellites. Starting from an initial estimate 50 km away from its true position, the receiver converged to a final two-dimensional (2D) position error of 30.3 m.
{"title":"On the Fundamental Tracking Performance and Design Considerations of Radio Navigation","authors":"Sharbel Kozhaya;Zaher M. Kassas","doi":"10.1109/JSAC.2024.3413998","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413998","url":null,"abstract":"The fundamental tracking performance in radio navigation is characterized, leading to optimal receiver design considerations. First, a generalized beacon model is proposed and its sufficient, salient parameters are defined. Second, closed-form approximations of the delay, Doppler, Doppler stretch, and Doppler rate ambiguity functions (AFs) and a generalized coherent integration efficiency model are proposed. Third, design considerations and optimal coherent processing interval (CPI) length selection are presented, based on the complete navigation framework, entailing the: (i) beacon’s parameters, (ii) channel dynamics between the transmitter and receiver, (iii) employed clocks by the transmitter and receiver, (iv) search space adopted in the acquisition stage, and (v) dynamical model’s order employed in the tracking stage. Fourth, the acquisition and tracking stages of the navigation receiver architecture are discussed. Fifth, three sets of experimental results are presented validating the proposed closed-form approximation of the Doppler stretch and Doppler rate AFs and demonstrating the performance of a receiver tuned by the proposed design considerations in acquiring, tracking, and localization, namely: (i) aircraft tracking of terrestrial 4G signals, (ii) stationary receiver tracking of Starlink low Earth orbit (LEO) signals, and (iii) stationary receiver localization with Starlink and OneWeb LEO signals. For the Starlink and OneWeb receiver localization experiment, the receiver was capable of tracking the Doppler and carrier phase of 5 Starlink and 3 OneWeb LEO satellites. Starting from an initial estimate 50 km away from its true position, the receiver converged to a final two-dimensional (2D) position error of 30.3 m.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013186","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3413963
Shichen Zhang;Huacheng Zeng;Y. Thomas Hou
The use of mobile phones while driving is a major source of distraction for vehicle drivers and has resulted in a large number of car accidents. While surveillance cameras can be used to detect the violation of phone use, they do not work well in some scenarios (e.g., darkness and blockage) and may raise privacy concerns. In this paper, we present PhoLoc, a roadside device to detect the violation of phone use in personal vehicles using the cellular signals emitted by cellphones. PhoLoc is equipped with two sensors: a multi-antenna radio receiver and a low-cost lidar. It jointly processes the multimodal data from the two sensors to estimate the relative location of a phone in a vehicle. The enabler of PhoLoc is a new near-field localization scheme, which is capable of estimating the location of a moving phone at a specific time moment by overhearing its cellular signals. We have built a prototype of PhoLoc and evaluated its performance in realistic scenarios. Experimental results show that PhoLoc achieves 4.2% false positive rate and 13.8% false negative rate in the detection of phone call violation.
{"title":"Is Driver on Phone Call? Mobile Device Localization Using Cellular Signal","authors":"Shichen Zhang;Huacheng Zeng;Y. Thomas Hou","doi":"10.1109/JSAC.2024.3413963","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413963","url":null,"abstract":"The use of mobile phones while driving is a major source of distraction for vehicle drivers and has resulted in a large number of car accidents. While surveillance cameras can be used to detect the violation of phone use, they do not work well in some scenarios (e.g., darkness and blockage) and may raise privacy concerns. In this paper, we present PhoLoc, a roadside device to detect the violation of phone use in personal vehicles using the cellular signals emitted by cellphones. PhoLoc is equipped with two sensors: a multi-antenna radio receiver and a low-cost lidar. It jointly processes the multimodal data from the two sensors to estimate the relative location of a phone in a vehicle. The enabler of PhoLoc is a new near-field localization scheme, which is capable of estimating the location of a moving phone at a specific time moment by overhearing its cellular signals. We have built a prototype of PhoLoc and evaluated its performance in realistic scenarios. Experimental results show that PhoLoc achieves 4.2% false positive rate and 13.8% false negative rate in the detection of phone call violation.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013422","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3414000
Zhinan Hu;Xin Chen;Zhenyu Zhou;Shahid Mumtaz
Precisely predicting the location of the user in a Global-Navigation-Satellite-System-degraded environment is a highly challenging task. Localization based on cellular signal fingerprints is one of the promising solutions to this problem and has attracted increasing attention. Long Term Evolution (LTE) signal is popularly utilized for localization due to its global usage, extensive urban coverage, and favorable signal properties. This paper proposes a new multiband multicell Reference Signal Received Power (MBMC-R) fingerprint, which properly fuses LTE signals’ carrier band information, the physical cell identifier information, and RSRP values. Next, a sequential block-matching weight K nearest neighbor algorithm with a cosine similarity criterion is specially designed for performing the pattern-matching localization with the MBMC-R fingerprint. The proposed method also includes the derivation of the Cramer-Rao lower bound, which reveals the impact of various factors on the lower bound of position error. Simulation and on-field experiments prove the performance superiority over other fingerprint localization algorithms reported in the literature.
{"title":"Localization With Cellular Signal RSRP Fingerprint of Multiband and Multicell","authors":"Zhinan Hu;Xin Chen;Zhenyu Zhou;Shahid Mumtaz","doi":"10.1109/JSAC.2024.3414000","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3414000","url":null,"abstract":"Precisely predicting the location of the user in a Global-Navigation-Satellite-System-degraded environment is a highly challenging task. Localization based on cellular signal fingerprints is one of the promising solutions to this problem and has attracted increasing attention. Long Term Evolution (LTE) signal is popularly utilized for localization due to its global usage, extensive urban coverage, and favorable signal properties. This paper proposes a new multiband multicell Reference Signal Received Power (MBMC-R) fingerprint, which properly fuses LTE signals’ carrier band information, the physical cell identifier information, and RSRP values. Next, a sequential block-matching weight K nearest neighbor algorithm with a cosine similarity criterion is specially designed for performing the pattern-matching localization with the MBMC-R fingerprint. The proposed method also includes the derivation of the Cramer-Rao lower bound, which reveals the impact of various factors on the lower bound of position error. Simulation and on-field experiments prove the performance superiority over other fingerprint localization algorithms reported in the literature.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013419","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3413981
Ang Chen;Li Chen;Yunfei Chen;Nan Zhao;Changsheng You
Positioning and sensing over wireless networks are imperative for many emerging applications. However, since traditional wireless channel models over-simplify the user equipment (UE) as a point target, they cannot be used for sensing the attitude of the UE, which is typically described by the spatial orientation. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cramér-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation.
{"title":"Near-Field Positioning and Attitude Sensing Based on Electromagnetic Propagation Modeling","authors":"Ang Chen;Li Chen;Yunfei Chen;Nan Zhao;Changsheng You","doi":"10.1109/JSAC.2024.3413981","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413981","url":null,"abstract":"Positioning and sensing over wireless networks are imperative for many emerging applications. However, since traditional wireless channel models over-simplify the user equipment (UE) as a point target, they cannot be used for sensing the attitude of the UE, which is typically described by the spatial orientation. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cramér-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013380","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3414001
Fan Zhang;Tianqi Mao;Ruiqi Liu;Zhu Han;Sheng Chen;Zhaocheng Wang
Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communication simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communication and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communication according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a ‘locally’ perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs.
正交频分复用（OFDM）已被广泛认为是 5G 无线网络的代表波形，可直接支持现有基础设施的传感/定位功能。为了在支持高速通信的同时保证卓越的传感/定位精度，双重功能往往会因设计要求的不同而分配给不同的资源要素（RE）。这就需要在时间、频率、功率和延迟-多普勒域对资源分配/波形设计进行优化。因此，本文提出了两种跨域波形优化策略，分别遵循以通信为中心和以传感为中心的标准，实现基于 OFDM 的通信和传感/定位的有效融合。在以通信为中心的设计中，为了最大限度地提高可实现的数据传输速率，根据事先对通信信道的了解，将一部分 RE 优化分配用于通信。然后将剩余的 RE 用于传感/定位，通过优化其功率频率和相位频率特性来抑制侧叶电平和峰均功率比，从而提高传感性能。在以传感为中心的设计中，通过调整相关区域（RoI）内模糊函数的单元格，确保 "局部 "完美的自相关特性，从而实现精确的传感和定位。然后，对 RoI 以外的无关单元（可随时确定传感功率分配）进行通信功率分配优化，以提高可实现的数据传输速率。数值结果证明了拟议波形设计的优越性。
{"title":"Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning","authors":"Fan Zhang;Tianqi Mao;Ruiqi Liu;Zhu Han;Sheng Chen;Zhaocheng Wang","doi":"10.1109/JSAC.2024.3414001","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3414001","url":null,"abstract":"Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communication simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communication and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communication according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a ‘locally’ perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013232","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3413996
Ziqin Zhou;Xiaoyang Li;Guangxu Zhu;Jie Xu;Kaibin Huang;Shuguang Cui
In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integrated sensing and communication (ISAC) technique exploits the sensing and communication functionalities of signals, while the simultaneous wireless information and power transfer (SWIPT) techniques utilizes the same signals as the carriers for both information and power delivery. The further combination of ISAC and SWIPT leads to the advanced technology namely integrated sensing, communication, and power transfer (ISCPT). In this paper, a multi-user multiple-input multiple-output (MIMO) ISCPT system is considered, where a base station equipped with multiple antennas transmits messages to multiple information receivers (IRs), transfers power to multiple energy receivers (ERs), and senses a target simultaneously. The sensing target can be regarded as a point or an extended surface. When the locations of IRs and ERs are separated, the MIMO beamforming designs are optimized to improve the sensing performance while meeting the communication and power transfer requirements. The resultant non-convex optimization problems are solved based on a series of techniques including Schur complement transformation and rank reduction. Moreover, when the IRs and ERs are co-located, the power splitting factors are jointly optimized together with the beamformers to balance the performance of communication and power transfer. To better understand the performance of ISCPT, the target positioning problem is further investigated. Simulations are conducted to verify the effectiveness of our proposed designs, which also reveal a performance tradeoff among sensing, communication, and power transfer.
在第六代（6G）网络中，大规模低功耗设备有望感知环境并传输大量数据。为了提高无线电资源效率，集成传感和通信（ISAC）技术利用了信号的传感和通信功能，而同步无线信息和功率传输（SWIPT）技术则利用相同的信号作为信息和功率传输的载体。ISAC 和 SWIPT 的进一步结合产生了先进的综合传感、通信和功率传输（ISCPT）技术。本文考虑的是一种多用户多输入多输出（MIMO）ISCPT 系统，在该系统中，配备多个天线的基站向多个信息接收器（IR）发送信息，向多个能量接收器（ER）传输功率，并同时感知目标。感知目标可以是一个点，也可以是一个扩展面。当 IR 和 ER 的位置分离时，需要优化 MIMO 波束成形设计，以提高传感性能，同时满足通信和功率传输要求。由此产生的非凸优化问题是基于一系列技术求解的，包括舒尔补码变换和秩缩减。此外，当 IR 和 ER 位于同一位置时，功率分配系数将与波束成形器共同优化，以平衡通信和功率传输性能。为了更好地理解 ISCPT 的性能，我们进一步研究了目标定位问题。仿真验证了我们提出的设计方案的有效性，同时也揭示了传感、通信和功率传输之间的性能权衡。
{"title":"Integrating Sensing, Communication, and Power Transfer: Multiuser Beamforming Design","authors":"Ziqin Zhou;Xiaoyang Li;Guangxu Zhu;Jie Xu;Kaibin Huang;Shuguang Cui","doi":"10.1109/JSAC.2024.3413996","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413996","url":null,"abstract":"In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integrated sensing and communication (ISAC) technique exploits the sensing and communication functionalities of signals, while the simultaneous wireless information and power transfer (SWIPT) techniques utilizes the same signals as the carriers for both information and power delivery. The further combination of ISAC and SWIPT leads to the advanced technology namely integrated sensing, communication, and power transfer (ISCPT). In this paper, a multi-user multiple-input multiple-output (MIMO) ISCPT system is considered, where a base station equipped with multiple antennas transmits messages to multiple information receivers (IRs), transfers power to multiple energy receivers (ERs), and senses a target simultaneously. The sensing target can be regarded as a point or an extended surface. When the locations of IRs and ERs are separated, the MIMO beamforming designs are optimized to improve the sensing performance while meeting the communication and power transfer requirements. The resultant non-convex optimization problems are solved based on a series of techniques including Schur complement transformation and rank reduction. Moreover, when the IRs and ERs are co-located, the power splitting factors are jointly optimized together with the beamformers to balance the performance of communication and power transfer. To better understand the performance of ISCPT, the target positioning problem is further investigated. Simulations are conducted to verify the effectiveness of our proposed designs, which also reveal a performance tradeoff among sensing, communication, and power transfer.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142013415","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3413968
Kyuwon Han;Seung Min Yu;Seong-Lyun Kim;Seung-Woo Ko
A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This paper aims to propose a novel integration technique using a graph neural network called Mobility-INduced Graph LEarning (MINGLE), which is designed based on two types of graphs made by capturing different user mobility features. Specifically, considering sequential measurement points (MPs) as nodes, a user’s regular mobility pattern allows us to connect neighbor MPs as edges, called time-driven mobility graph (TMG). Second, a user’s relatively straight transition at a constant pace when moving from one position to another can be captured by connecting the nodes on each path, called a direction-driven mobility graph (DMG). Then, we can design graph convolution network (GCN)-based cross-graph learning, where two different GCN models for TMG and DMG are jointly trained by feeding different input features created by WiFi RTTs yet sharing their weights. Besides, the loss function includes a mobility regularization term such that the differences between adjacent location estimates should be less variant due to the user’s stable moving pace. Noting that the regularization term does not require ground-truth location, MINGLE can be designed under semi- and self-supervised learning frameworks. The proposed MINGLE’s effectiveness is extensively verified through field experiments, showing a better positioning accuracy than benchmarks, say mean absolute errors (MAEs) being 1.510 (m) and 1.077 (m) for self- and semi-supervised learning cases, respectively.
{"title":"Mobility-Induced Graph Learning for WiFi Positioning","authors":"Kyuwon Han;Seung Min Yu;Seong-Lyun Kim;Seung-Woo Ko","doi":"10.1109/JSAC.2024.3413968","DOIUrl":"10.1109/JSAC.2024.3413968","url":null,"abstract":"A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This paper aims to propose a novel integration technique using a graph neural network called Mobility-INduced Graph LEarning (MINGLE), which is designed based on two types of graphs made by capturing different user mobility features. Specifically, considering sequential measurement points (MPs) as nodes, a user’s regular mobility pattern allows us to connect neighbor MPs as edges, called time-driven mobility graph (TMG). Second, a user’s relatively straight transition at a constant pace when moving from one position to another can be captured by connecting the nodes on each path, called a direction-driven mobility graph (DMG). Then, we can design graph convolution network (GCN)-based cross-graph learning, where two different GCN models for TMG and DMG are jointly trained by feeding different input features created by WiFi RTTs yet sharing their weights. Besides, the loss function includes a mobility regularization term such that the differences between adjacent location estimates should be less variant due to the user’s stable moving pace. Noting that the regularization term does not require ground-truth location, MINGLE can be designed under semi- and self-supervised learning frameworks. The proposed MINGLE’s effectiveness is extensively verified through field experiments, showing a better positioning accuracy than benchmarks, say mean absolute errors (MAEs) being 1.510 (m) and 1.077 (m) for self- and semi-supervised learning cases, respectively.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933192","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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