Visible light positioning (VLP) is endowed with high accuracy in indoor scenarios. However, the positioning algorithms require plenty of beacon light-emitting diode (LED) coordinates stored in databases, which are expensive to obtain by manual measurements. To circumvent such laborious efforts, we propose a two-step automatic scheme for simultaneous VLP and LED database construction. Specifically, in the first step, a receiver with a photodiode (PD) array samples the optical signals from few benchmark LEDs to locate itself. In the second step, the receiver estimates the unknown beacon LED coordinates through its own locations and the beacon LED signals. For the proposed two-step scheme, we derive closed-form error expressions for the beacon LED coordinates to evaluate the benchmark LEDs’ arrangement and the sampling places. Simulation results agree with the analytical error expressions and reveal that the proposed scheme can achieve centimeter-level accuracy with reasonable transmit powers. Experimental results from the hardware platform verify the feasibility of the scheme. The proposed scheme can circumvent laborious manual measurements and allow the LED database to “grow” while the receivers wander and more receivers enter.
可见光定位（VLP）在室内场景中具有很高的精确度。然而，定位算法需要大量存储在数据库中的信标发光二极管（LED）坐标，而人工测量获取这些坐标的成本很高。为了避免这种费力的工作，我们提出了一种分两步同时构建 VLP 和 LED 数据库的自动方案。具体来说，在第一步中，带有光电二极管（PD）阵列的接收器对来自少数基准 LED 的光信号进行采样，以确定自己的位置。第二步，接收器通过自身位置和信标 LED 信号估算未知信标 LED 坐标。对于建议的两步方案，我们推导出信标 LED 坐标的闭式误差表达式，以评估基准 LED 的排列和采样位置。仿真结果与分析误差表达式一致，并揭示了所提出的方案可以在合理的发射功率下实现厘米级精度。硬件平台的实验结果验证了该方案的可行性。建议的方案可以避免费力的人工测量，并允许 LED 数据库 "增长"，同时接收器也在不断变化，并有更多的接收器进入。
{"title":"A Simultaneous Visible Light Positioning and LED Database Construction Scheme","authors":"Canran Shi;Kehan Zhang;Bingcheng Zhu;Zaichen Zhang","doi":"10.1109/JSAC.2024.3413959","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413959","url":null,"abstract":"Visible light positioning (VLP) is endowed with high accuracy in indoor scenarios. However, the positioning algorithms require plenty of beacon light-emitting diode (LED) coordinates stored in databases, which are expensive to obtain by manual measurements. To circumvent such laborious efforts, we propose a two-step automatic scheme for simultaneous VLP and LED database construction. Specifically, in the first step, a receiver with a photodiode (PD) array samples the optical signals from few benchmark LEDs to locate itself. In the second step, the receiver estimates the unknown beacon LED coordinates through its own locations and the beacon LED signals. For the proposed two-step scheme, we derive closed-form error expressions for the beacon LED coordinates to evaluate the benchmark LEDs’ arrangement and the sampling places. Simulation results agree with the analytical error expressions and reveal that the proposed scheme can achieve centimeter-level accuracy with reasonable transmit powers. Experimental results from the hardware platform verify the feasibility of the scheme. The proposed scheme can circumvent laborious manual measurements and allow the LED database to “grow” while the receivers wander and more receivers enter.","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":"142021627","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}
We present Eye-Beam, a programmable platform for integrated communication and sensing. Eye-Beam leverages the hardware and processing required for standard millimeter-wave (mmWave) 5G directional communications to enable sensing functions. Specifically, our platform (1) receives and synchronizes to the data frame of broadcast 5G signals, (2) extracts directional communication features, creating a tensor of spatial information, and (3) utilizes this data as input to a DNN that infers the presence of specific objects in the propagation environment. Eye-Beam includes a programmable 28 GHz 64-element phased array, an SDR, and custom FPGA-based firmware. Eye-Beam’s key capabilities and metrics include (i) synchronization of I/Q data (up to 200 MSPS) with beam steering (among 9,601 beams) with 10 ns accuracy; (ii) a signal processing pipeline that extracts communication features such as the SNR and channel response from received 5G waveforms; and (iii) system orchestration that synchronizes the receiver (RX) to the 5G frame structure of the base station (gNodeB) and maintains it within a worst-case OFDM cyclic prefix of $0.29~mu $ s. Eye-Beam is also able to emulate gNodeB transmissions. We demonstrate Eye-Beam’s performance by showcasing its communication capability (decoding up to 64-QAM), as well as its performance as a channel sounder (extracting detailed directional 5G features in 2,401 beam directions within just 20 ms). We then, for the first time, demonstrate AI-based object classification only using the directional communication features derived by Eye-Beam from ambient mmWave 5G signals transmitted by a gNodeB. Six object classes, including 4 distinct objects concealed in a backpack, are classified with 98% accuracy in an indoor environment.
{"title":"Eye-Beam: A mmWave 5G-Compliant Platform for Integrated Communications and Sensing Enabling AI-Based Object Recognition","authors":"Arun Paidimarri;Asaf Tzadok;Sara Garcia Sanchez;Atsutse Kludze;Alexandra Gallyas-Sanhueza;Alberto Valdes-Garcia","doi":"10.1109/JSAC.2024.3413978","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413978","url":null,"abstract":"We present Eye-Beam, a programmable platform for integrated communication and sensing. Eye-Beam leverages the hardware and processing required for standard millimeter-wave (mmWave) 5G directional communications to enable sensing functions. Specifically, our platform (1) receives and synchronizes to the data frame of broadcast 5G signals, (2) extracts directional communication features, creating a tensor of spatial information, and (3) utilizes this data as input to a DNN that infers the presence of specific objects in the propagation environment. Eye-Beam includes a programmable 28 GHz 64-element phased array, an SDR, and custom FPGA-based firmware. Eye-Beam’s key capabilities and metrics include (i) synchronization of I/Q data (up to 200 MSPS) with beam steering (among 9,601 beams) with 10 ns accuracy; (ii) a signal processing pipeline that extracts communication features such as the SNR and channel response from received 5G waveforms; and (iii) system orchestration that synchronizes the receiver (RX) to the 5G frame structure of the base station (gNodeB) and maintains it within a worst-case OFDM cyclic prefix of \u0000<inline-formula> <tex-math>$0.29~mu $ </tex-math></inline-formula>\u0000s. Eye-Beam is also able to emulate gNodeB transmissions. We demonstrate Eye-Beam’s performance by showcasing its communication capability (decoding up to 64-QAM), as well as its performance as a channel sounder (extracting detailed directional 5G features in 2,401 beam directions within just 20 ms). We then, for the first time, demonstrate AI-based object classification only using the directional communication features derived by Eye-Beam from ambient mmWave 5G signals transmitted by a gNodeB. Six object classes, including 4 distinct objects concealed in a backpack, are classified with 98% accuracy in an indoor environment.","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":"142013381","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.2024.3413985
Vishnu V. Ratnam;Bilal Sadiq;Hao Chen;Wei Sun;Shunyao Wu;Boon Loong Ng;Jianzhong Zhang
Although Wi-Fi is an ideal technology for many ranging applications, the performance of current methods is limited by the system bandwidth, leading to low accuracy of ~1 m. For many applications, measuring differential range, viz., the change in the range between adjacent measurements, is sufficient. Correspondingly, this work proposes WiDRa - a Wi-Fi based Differential Ranging solution that provides differential range estimates by using the sum-carrier-phase information. The proposed method is not limited by system bandwidth and can track range changes even smaller than the carrier wavelength. The proposed method is first theoretically justified, while taking into consideration the various hardware impairments affecting Wi-Fi chips. In the process, methods to isolate the sum-carrier phase from the hardware impairments are proposed. Extensive simulation results show that WiDRa can achieve a differential range estimation root-mean-square-error (RMSE) of $approx 1$ mm in channels with a Rician-factor $geq 7$ (a $100 times $ improvement to existing methods). The proposed methods are also validated on off-the-shelf Wi-Fi hardware to demonstrate feasibility, where they achieve an RMSE of <1 mm in the differential range. Finally, limitations of current investigation and future directions of exploration are suggested, to further tap into the potential of WiDRa.
{"title":"WiDRa: Enabling Millimeter-Level Differential Ranging Accuracy in Wi-Fi Using Carrier Phase","authors":"Vishnu V. Ratnam;Bilal Sadiq;Hao Chen;Wei Sun;Shunyao Wu;Boon Loong Ng;Jianzhong Zhang","doi":"10.1109/JSAC.2024.3413985","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413985","url":null,"abstract":"Although Wi-Fi is an ideal technology for many ranging applications, the performance of current methods is limited by the system bandwidth, leading to low accuracy of ~1 m. For many applications, measuring differential range, viz., the change in the range between adjacent measurements, is sufficient. Correspondingly, this work proposes WiDRa - a Wi-Fi based Differential Ranging solution that provides differential range estimates by using the sum-carrier-phase information. The proposed method is not limited by system bandwidth and can track range changes even smaller than the carrier wavelength. The proposed method is first theoretically justified, while taking into consideration the various hardware impairments affecting Wi-Fi chips. In the process, methods to isolate the sum-carrier phase from the hardware impairments are proposed. Extensive simulation results show that WiDRa can achieve a differential range estimation root-mean-square-error (RMSE) of \u0000<inline-formula> <tex-math>$approx 1$ </tex-math></inline-formula>\u0000 mm in channels with a Rician-factor \u0000<inline-formula> <tex-math>$geq 7$ </tex-math></inline-formula>\u0000 (a \u0000<inline-formula> <tex-math>$100 times $ </tex-math></inline-formula>\u0000 improvement to existing methods). The proposed methods are also validated on off-the-shelf Wi-Fi hardware to demonstrate feasibility, where they achieve an RMSE of <1 mm in the differential range. Finally, limitations of current investigation and future directions of exploration are suggested, to further tap into the potential of WiDRa.","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":"142013295","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}
In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on end-to-end processing solutions, from raw I/Q samples, via channel parameter estimation to user equipment (UE) and landmark location information in millimeter-wave (mmWave) networks, with minimal prior knowledge. Firstly, we propose a new multipath channel parameter estimation solution that operates directly with beam reference signal received power (BRSRP) measurements, alleviating the need to know the true antenna beampatterns or the underlying beamforming weights. Additionally, the method has built-in robustness against unavoidable antenna sidelobes. Secondly, we propose new snapshot SLAM algorithms that have increased robustness and identifiability compared to prior art, in practical built environments with complex clutter and multi-bounce propagation scenarios, and do not rely on any a priori motion model. The performance of the proposed methods is assessed at the 60GHz mmWave band, via both realistic ray-tracing evaluations as well as true experimental measurements, in an indoor environment. A wide set of offered results demonstrate the improved performance, compared to the relevant prior art, in terms of the channel parameter estimation as well as the end-to-end SLAM performance. Finally, the article provides the measured 60GHz data openly available for the research community, facilitating results reproducibility as well as further algorithm development.
在本文中，我们探讨了蜂窝双稳态同步定位和映射（SLAM）这一适时的主题，特别关注端到端处理解决方案，从原始 I/Q 样本到信道参数估计，再到毫米波（mmWave）网络中的用户设备（UE）和地标位置信息，都只需要最少的先验知识。首先，我们提出了一种新的多径信道参数估计解决方案，该方案可直接利用波束参考信号接收功率（BRSRP）测量值进行操作，无需了解真实的天线波束赋形或基本波束成形权重。此外，该方法还具有内置鲁棒性，可抵御不可避免的天线侧扰。其次，我们提出了新的快照 SLAM 算法，与现有技术相比，该算法在具有复杂杂波和多弹跳传播场景的实际建筑环境中具有更高的鲁棒性和可识别性，并且不依赖任何先验运动模型。在 60GHz 毫米波频段，通过现实光线追踪评估以及室内环境中的真实实验测量，对所提方法的性能进行了评估。提供的大量结果表明，与相关的现有技术相比，该方法在信道参数估计和端到端 SLAM 性能方面都有所改进。最后，文章为研究界提供了公开的 60GHz 测量数据，促进了结果的可重复性以及进一步的算法开发。
{"title":"Millimeter-Wave Radio SLAM: End-to-End Processing Methods and Experimental Validation","authors":"Elizaveta Rastorgueva-Foi;Ossi Kaltiokallio;Yu Ge;Matias Turunen;Jukka Talvitie;Bo Tan;Musa Furkan Keskin;Henk Wymeersch;Mikko Valkama","doi":"10.1109/JSAC.2024.3413995","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413995","url":null,"abstract":"In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on end-to-end processing solutions, from raw I/Q samples, via channel parameter estimation to user equipment (UE) and landmark location information in millimeter-wave (mmWave) networks, with minimal prior knowledge. Firstly, we propose a new multipath channel parameter estimation solution that operates directly with beam reference signal received power (BRSRP) measurements, alleviating the need to know the true antenna beampatterns or the underlying beamforming weights. Additionally, the method has built-in robustness against unavoidable antenna sidelobes. Secondly, we propose new snapshot SLAM algorithms that have increased robustness and identifiability compared to prior art, in practical built environments with complex clutter and multi-bounce propagation scenarios, and do not rely on any a priori motion model. The performance of the proposed methods is assessed at the 60GHz mmWave band, via both realistic ray-tracing evaluations as well as true experimental measurements, in an indoor environment. A wide set of offered results demonstrate the improved performance, compared to the relevant prior art, in terms of the channel parameter estimation as well as the end-to-end SLAM performance. Finally, the article provides the measured 60GHz data openly available for the research community, facilitating results reproducibility as well as further algorithm development.","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":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10556695","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021626","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3413973
Biwei Li;Xianbin Wang;Fang Fang
The proliferation of wireless-enabled industrial applications highlights the growing importance of Integrated Sensing and Communication (ISAC) for concurrent provisioning of environment sensing and data transmission capabilities. However, the resource-hungry nature of sensing processes, coupled with competing demands from coexisting users, poses the fundamental challenge of effective and fair resource allocation in multi-user ISAC systems. To address this challenge, we propose a value of service (VoS)-oriented resource allocation scheme for concurrent heterogeneous service provisioning in a multi-user collaborative ISAC system. Specifically, a performance indicator VoS is utilized to guide system-wide effective resource allocation while guaranteeing fairness among all ISAC users. Specifically, we formulate the multi-user resource allocation problem as a bargaining game-based model and tackle it with an iterative algorithm to attain the Nash equilibrium. In each iteration, the allocation of power and bandwidth resources is optimized by solving the Lagrangian dual problem. Numerical simulations are performed under varying resource conditions, service demands, and channel states. The results demonstrate the superiority of the proposed scheme over non-collaborative alternatives and the other two benchmark schemes.
无线工业应用的激增凸显了综合传感与通信（ISAC）在同时提供环境传感和数据传输能力方面日益增长的重要性。然而，传感过程对资源的苛刻要求，加上共存用户的竞争性需求，给多用户 ISAC 系统的有效和公平资源分配带来了根本性的挑战。为了应对这一挑战，我们提出了一种面向服务价值（VoS）的资源分配方案，用于多用户协作 ISAC 系统中的并发异构服务供应。具体来说，我们利用性能指标 VoS 来指导全系统的有效资源分配，同时保证所有 ISAC 用户之间的公平性。具体来说，我们将多用户资源分配问题表述为一个基于讨价还价博弈的模型，并通过迭代算法来实现纳什均衡。在每次迭代中，通过求解拉格朗日对偶问题来优化功率和带宽资源的分配。在不同的资源条件、服务需求和信道状态下进行了数值模拟。结果表明，建议的方案优于非协作方案和其他两个基准方案。
{"title":"Maximizing the Value of Service Provisioning in Multi-User ISAC Systems Through Fairness Guaranteed Collaborative Resource Allocation","authors":"Biwei Li;Xianbin Wang;Fang Fang","doi":"10.1109/JSAC.2024.3413973","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413973","url":null,"abstract":"The proliferation of wireless-enabled industrial applications highlights the growing importance of Integrated Sensing and Communication (ISAC) for concurrent provisioning of environment sensing and data transmission capabilities. However, the resource-hungry nature of sensing processes, coupled with competing demands from coexisting users, poses the fundamental challenge of effective and fair resource allocation in multi-user ISAC systems. To address this challenge, we propose a value of service (VoS)-oriented resource allocation scheme for concurrent heterogeneous service provisioning in a multi-user collaborative ISAC system. Specifically, a performance indicator VoS is utilized to guide system-wide effective resource allocation while guaranteeing fairness among all ISAC users. Specifically, we formulate the multi-user resource allocation problem as a bargaining game-based model and tackle it with an iterative algorithm to attain the Nash equilibrium. In each iteration, the allocation of power and bandwidth resources is optimized by solving the Lagrangian dual problem. Numerical simulations are performed under varying resource conditions, service demands, and channel states. The results demonstrate the superiority of the proposed scheme over non-collaborative alternatives and the other two benchmark schemes.","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":"142013253","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.2024.3413960
Renzhi Yuan;Siming Wang;Gang Liu;Mugen Peng
Traditional optical positioning techniques employing visible light signals or infrared light signals require line-of-sight links between transmitters and receivers. The wireless positioning techniques using ultraviolet (UV) signals can enjoy both non-line-of-sight (NLOS) positioning ability and immunity to electromagnetic jamming. In this work, we focus on NLOS UV positioning techniques using linearly-arrayed photon-counting receivers. We first derive the geometrical and physical constrains for the NLOS UV positioning using linearly-arrayed receivers. We then derive the analytical relation between location parameters and pointing parameters of unknown transmitter and propose a NLOS UV positioning method with acceptable computational complexity. We further derive the Cramér-Rao bounds for the positioning method when the separate distance between adjacent receivers equals zero. Numerical results demonstrate that the proposed NLOS UV positioning methods using photon-counting receivers can achieve a distance error less than 2 m when the transmitting elevation angle is greater than 30 degrees and the separate distance is greater than 2 m. Besides, we demonstrate that at least three receivers are required to avoid multiple solution problem; and three receivers are enough for achieving an acceptable positioning error for NLOS UV positioning using photon-counting receivers.
{"title":"Non-Line-of-Sight Ultraviolet Positioning Using Linearly-Arrayed Photon-Counting Receivers","authors":"Renzhi Yuan;Siming Wang;Gang Liu;Mugen Peng","doi":"10.1109/JSAC.2024.3413960","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413960","url":null,"abstract":"Traditional optical positioning techniques employing visible light signals or infrared light signals require line-of-sight links between transmitters and receivers. The wireless positioning techniques using ultraviolet (UV) signals can enjoy both non-line-of-sight (NLOS) positioning ability and immunity to electromagnetic jamming. In this work, we focus on NLOS UV positioning techniques using linearly-arrayed photon-counting receivers. We first derive the geometrical and physical constrains for the NLOS UV positioning using linearly-arrayed receivers. We then derive the analytical relation between location parameters and pointing parameters of unknown transmitter and propose a NLOS UV positioning method with acceptable computational complexity. We further derive the Cramér-Rao bounds for the positioning method when the separate distance between adjacent receivers equals zero. Numerical results demonstrate that the proposed NLOS UV positioning methods using photon-counting receivers can achieve a distance error less than 2 m when the transmitting elevation angle is greater than 30 degrees and the separate distance is greater than 2 m. Besides, we demonstrate that at least three receivers are required to avoid multiple solution problem; and three receivers are enough for achieving an acceptable positioning error for NLOS UV positioning using photon-counting receivers.","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":"142021674","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.2024.3413977
Myeung Suk Oh;Anindya Bijoy Das;Taejoon Kim;David J. Love;Christopher G. Brinton
Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational complexity coming from processing high-dimensional features can be prohibitive for mobile applications. In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP. P-NN’s feature selection strategy is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We improve P-NN’s learning ability by intelligently processing two different types of inputs: sparse image and measurement matrices. Specifically, we implement a self-attention layer to reinforce the training ability of our network. We also develop a technique to adapt feature space size, optimizing over the expected information gain and the classification capability quantified with information-theoretic measures on signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP). In particular, we find that P-NN achieves a large improvement in performance for low SNR, as unnecessary measurements are discarded in our minimum description features.
{"title":"Minimum Description Feature Selection for Complexity Reduction in Machine Learning-Based Wireless Positioning","authors":"Myeung Suk Oh;Anindya Bijoy Das;Taejoon Kim;David J. Love;Christopher G. Brinton","doi":"10.1109/JSAC.2024.3413977","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413977","url":null,"abstract":"Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational complexity coming from processing high-dimensional features can be prohibitive for mobile applications. In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP. P-NN’s feature selection strategy is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We improve P-NN’s learning ability by intelligently processing two different types of inputs: sparse image and measurement matrices. Specifically, we implement a self-attention layer to reinforce the training ability of our network. We also develop a technique to adapt feature space size, optimizing over the expected information gain and the classification capability quantified with information-theoretic measures on signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP). In particular, we find that P-NN achieves a large improvement in performance for low SNR, as unnecessary measurements are discarded in our minimum description features.","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":"142021595","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.2024.3413961
Shilian Zheng;Jiacheng Hu;Luxin Zhang;Kunfeng Qiu;Jie Chen;Peihan Qi;Zhijin Zhao;Xiaoniu Yang
Frequency Modulation (FM) broadcast signals, regarded as opportunistic signals, hold significant potential for indoor and outdoor positioning applications. The existing FM-based positioning methods primarily rely on Received Signal Strength (RSS) for positioning, the accuracy of which needs improvement. In this paper, we introduce FM-Pnet, an end-to-end FM-based positioning method that leverages deep learning. This method utilizes the time-frequency representation of FM signals as network input, enabling automatically learning of deep features for positioning. We also propose two strategies, noise injection and enriching training samples, to enhance the model’s generalization performance over long time spans. We construct datasets for both indoor and outdoor scenarios and conduct extensive experiments to validate the performance of our proposed method. Experimental results demonstrate that FM-Pnet significantly outperforms traditional RSS-based positioning methods in terms of both positioning accuracy and stability.
{"title":"FM-Based Positioning via Deep Learning","authors":"Shilian Zheng;Jiacheng Hu;Luxin Zhang;Kunfeng Qiu;Jie Chen;Peihan Qi;Zhijin Zhao;Xiaoniu Yang","doi":"10.1109/JSAC.2024.3413961","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413961","url":null,"abstract":"Frequency Modulation (FM) broadcast signals, regarded as opportunistic signals, hold significant potential for indoor and outdoor positioning applications. The existing FM-based positioning methods primarily rely on Received Signal Strength (RSS) for positioning, the accuracy of which needs improvement. In this paper, we introduce FM-Pnet, an end-to-end FM-based positioning method that leverages deep learning. This method utilizes the time-frequency representation of FM signals as network input, enabling automatically learning of deep features for positioning. We also propose two strategies, noise injection and enriching training samples, to enhance the model’s generalization performance over long time spans. We construct datasets for both indoor and outdoor scenarios and conduct extensive experiments to validate the performance of our proposed method. Experimental results demonstrate that FM-Pnet significantly outperforms traditional RSS-based positioning methods in terms of both positioning accuracy and stability.","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":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10556685","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021675","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 : 2024-06-13DOI: 10.1109/JSAC.2024.3413988
Xu Chen;Khaled B. Letaief;Kaibin Huang
Sensing and edge artificial intelligence (AI) are two key features of the sixth-generation (6G) mobile networks. Their natural integration, termed Integrated sensing and edge AI (ISEA), is envisioned to automate wide-ranging Internet-of-Ting (IoT) applications. To achieve a high sensing accuracy, features of multiple sensor views are uploaded to an edge server for aggregation and inference using a large-scale AI model. The view aggregation is realized efficiently using over-the-air computing (AirComp), which also aggregates channels to suppress channel noise. As ISEA is at its nascent stage, there still lacks an analytical framework for quantifying the fundamental performance gains from view-and-channel aggregation, which motivates this work. Our framework is based on a well-established distribution model of multi-view sensing data where the classic Gaussian-mixture model is modified by adding sub-spaces matrices to represent individual sensor observation perspectives. Based on the model and linear classification, we study the End-to-End sensing (inference) uncertainty, a popular measure of inference accuracy, of the said ISEA system by a novel, tractable approach involving designing a scaling-tight uncertainty surrogate function, global discriminant gain, distribution of receive Signal-to-Noise Ratio (SNR), and channel induced discriminant loss. As a result, we prove that the E2E sensing uncertainty diminishes at an exponential rate as the number of views/sensors grows, where the rate is proportional to global discriminant gain. Given AirComp and channel distortion, we further show that the exponential scaling remains but the rate is reduced by a linear factor representing the channel induced discriminant loss. Furthermore, in the case of many spatial degrees of freedom, we benchmark AirComp against equally fast, traditional analog orthogonal access. The comparative performance analysis reveals a sensing-accuracy crossing point between the schemes corresponding to equal receive array size and sensor number. This leads to the proposal of a scheme for adaptive access-mode switching to enhance ISEA performance. Last, the insights from our framework are validated by experiments using a convolutional neural network model and real-world dataset.
{"title":"On the View-and-Channel Aggregation Gain in Integrated Sensing and Edge AI","authors":"Xu Chen;Khaled B. Letaief;Kaibin Huang","doi":"10.1109/JSAC.2024.3413988","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413988","url":null,"abstract":"Sensing and edge artificial intelligence (AI) are two key features of the sixth-generation (6G) mobile networks. Their natural integration, termed Integrated sensing and edge AI (ISEA), is envisioned to automate wide-ranging Internet-of-Ting (IoT) applications. To achieve a high sensing accuracy, features of multiple sensor views are uploaded to an edge server for aggregation and inference using a large-scale AI model. The view aggregation is realized efficiently using over-the-air computing (AirComp), which also aggregates channels to suppress channel noise. As ISEA is at its nascent stage, there still lacks an analytical framework for quantifying the fundamental performance gains from view-and-channel aggregation, which motivates this work. Our framework is based on a well-established distribution model of multi-view sensing data where the classic Gaussian-mixture model is modified by adding sub-spaces matrices to represent individual sensor observation perspectives. Based on the model and linear classification, we study the End-to-End sensing (inference) uncertainty, a popular measure of inference accuracy, of the said ISEA system by a novel, tractable approach involving designing a scaling-tight uncertainty surrogate function, global discriminant gain, distribution of receive Signal-to-Noise Ratio (SNR), and channel induced discriminant loss. As a result, we prove that the E2E sensing uncertainty diminishes at an exponential rate as the number of views/sensors grows, where the rate is proportional to global discriminant gain. Given AirComp and channel distortion, we further show that the exponential scaling remains but the rate is reduced by a linear factor representing the channel induced discriminant loss. Furthermore, in the case of many spatial degrees of freedom, we benchmark AirComp against equally fast, traditional analog orthogonal access. The comparative performance analysis reveals a sensing-accuracy crossing point between the schemes corresponding to equal receive array size and sensor number. This leads to the proposal of a scheme for adaptive access-mode switching to enhance ISEA performance. Last, the insights from our framework are validated by experiments using a convolutional neural network model and real-world dataset.","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":"142013421","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}
Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage and ergodic rate of sensing and communication performance under resource constraints. Then, we shed light on the fundamental limits of ISAC networks by presenting theoretical results for the coverage rate of the unified performance, taking into account the coupling effects of dual functions in coexistence networks. Further, we obtain the analytical formulations for evaluating the ergodic sensing rate constrained by the maximum communication rate, and the ergodic communication rate constrained by the maximum sensing rate. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from $1~text {km}^{-2}$ to $10~text {km}^{-2}$ can boost the ISAC coverage rate from 1.4% to 39.8%. Further, results also reveal that with the increase of the constrained sensing rate, the ergodic communication rate improves significantly, but the reverse is not obvious.
{"title":"Coverage and Rate Analysis for Integrated Sensing and Communication Networks","authors":"Xu Gan;Chongwen Huang;Zhaohui Yang;Xiaoming Chen;Jiguang He;Zhaoyang Zhang;Chau Yuen;Yong Liang Guan;Mérouane Debbah","doi":"10.1109/JSAC.2024.3413989","DOIUrl":"https://doi.org/10.1109/JSAC.2024.3413989","url":null,"abstract":"Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage and ergodic rate of sensing and communication performance under resource constraints. Then, we shed light on the fundamental limits of ISAC networks by presenting theoretical results for the coverage rate of the unified performance, taking into account the coupling effects of dual functions in coexistence networks. Further, we obtain the analytical formulations for evaluating the ergodic sensing rate constrained by the maximum communication rate, and the ergodic communication rate constrained by the maximum sensing rate. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from \u0000<inline-formula> <tex-math>$1~text {km}^{-2}$ </tex-math></inline-formula>\u0000 to \u0000<inline-formula> <tex-math>$10~text {km}^{-2}$ </tex-math></inline-formula>\u0000 can boost the ISAC coverage rate from 1.4% to 39.8%. Further, results also reveal that with the increase of the constrained sensing rate, the ergodic communication rate improves significantly, but the reverse is not obvious.","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":"142013379","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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