Pub Date : 2024-06-14DOI: 10.1109/JSAC.2024.3414628
Jiacheng Wang;Hongyang Du;Dusit Niyato;Zehui Xiong;Jiawen Kang;Bo Ai;Zhu Han;Dong In Kim
Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing.
ChatGPT 等开创性应用提高了对生成式人工智能（GAI）的研究兴趣。从本质上讲，GAI 不仅擅长内容生成，还擅长信号处理，可为无线传感提供支持。因此，我们推出了一种新颖的 GAI 辅助人流检测系统（G-HFD）。严谨地说，G-HFD 首先利用信道状态信息（CSI）来估计人流反射（HIR）传播路径长度变化的速度和加速度。然后，鉴于扩散模型的强大推理能力，我们提出了统一加权条件扩散模型（UW-CDM）来对估计结果进行去噪处理，从而实现对目标数量的检测。接下来，我们利用波长间隔均匀线性阵列获得的 CSI 来估计 HIR 的飞行时间和到达方向（DoA）。在此过程中，UW-CDM 解决了 DoA 频谱模糊的问题，确保了准确的 DoA 估计。最后，通过聚类，G-HFD 确定子流数量和每个子流中的目标数量，即子流大小。基于实际下行通信信号的评估表明，G-HFD 的子流大小检测准确率可达 91%。这不仅验证了 GAI 的有效性，也凸显了 GAI 在无线传感领域的巨大潜力。
{"title":"Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments","authors":"Jiacheng Wang;Hongyang Du;Dusit Niyato;Zehui Xiong;Jiawen Kang;Bo Ai;Zhu Han;Dong In Kim","doi":"10.1109/JSAC.2024.3414628","DOIUrl":"10.1109/JSAC.2024.3414628","url":null,"abstract":"Groundbreaking applications such as ChatGPT have heightened research interest in generative artificial intelligence (GAI). Essentially, GAI excels not only in content generation but also signal processing, offering support for wireless sensing. Hence, we introduce a novel GAI-assisted human flow detection system (G-HFD). Rigorously, G-HFD first uses the channel state information (CSI) to estimate the velocity and acceleration of propagation path length change of the human induced reflection (HIR). Then, given the strong inference ability of the diffusion model, we propose a unified weighted conditional diffusion model (UW-CDM) to denoise the estimation results, enabling detection of the number of targets. Next, we use the CSI obtained by a uniform linear array with wavelength spacing to estimate the HIR’s time of flight and direction of arrival (DoA). In this process, UW-CDM solves the problem of ambiguous DoA spectrum, ensuring accurate DoA estimation. Finally, through clustering, G-HFD determines the number of subflows and the number of targets in each subflow, i.e., the subflow size. The evaluation based on practical downlink communication signals shows G-HFD’s accuracy of subflow size detection can reach 91%. This validates its effectiveness and underscores the significant potential of GAI in the context of wireless sensing.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2737-2753"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933168","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}
For increasing Internet of Things (IoT) devices, 6G wireless technology aims for ubiquitous communications in which positioning services are necessary. Private spatial keyword-based positioning service is promising in 6G-oriented IoT since it positions users based on spatial locations and textual keywords while protecting user privacy. However, due to economic benefits or malicious attacks, positioning service providers may return erroneous or incomplete results, which cause tremendous economic damage and security threats, e.g., always assigning a selective driver for the specific car-hailing user. A technical challenge for extending existing private schemes to enable users to verify the correctness and completeness of positioning results is the distinctive positioning paradigm between compared spatial locations and matched textual keywords. This paper proposes a private and verifiable spatial keyword positioning scheme named VSpatial in 6G-oriented IoT. VSpatial enables users to verify the correctness and completeness of spatial keyword-based positioning results while preserving user privacy. The main inspiration for addressing the technical challenge is converting both spatial locations and textual keywords into an internal status, i.e., adapting comparison and matching to existence judging by multiple cryptographic tools, such as hierarchical cube and pseudorandom function. Based on this inspiration, we design a novel private authenticated data structure (named PVTree), and then propose two constructions of VSpatial, i.e., VSpatial-S and VSpatial-D, to suit static and dynamic environments, respectively. The core idea for adapting VSpatial-S to VSpatial-D is transferring one whole PVTree into multiple exponential-size partitions. Security analysis proves the security and verifiability of VSpatial. Theoretical and experimental evaluations show that VSpatial achieves faster-than-linear positioning efficiency and linear verification overhead.
{"title":"VSpatial: Enabling Private and Verifiable Spatial Keyword-Based Positioning in 6G-Oriented IoT","authors":"Weiting Zhang;Mingyang Zhao;Zhuoyu Sun;Chuan Zhang;Jinwen Liang;Liehuang Zhu;Song Guo","doi":"10.1109/JSAC.2024.3414605","DOIUrl":"10.1109/JSAC.2024.3414605","url":null,"abstract":"For increasing Internet of Things (IoT) devices, 6G wireless technology aims for ubiquitous communications in which positioning services are necessary. Private spatial keyword-based positioning service is promising in 6G-oriented IoT since it positions users based on spatial locations and textual keywords while protecting user privacy. However, due to economic benefits or malicious attacks, positioning service providers may return erroneous or incomplete results, which cause tremendous economic damage and security threats, e.g., always assigning a selective driver for the specific car-hailing user. A technical challenge for extending existing private schemes to enable users to verify the correctness and completeness of positioning results is the distinctive positioning paradigm between compared spatial locations and matched textual keywords. This paper proposes a private and verifiable spatial keyword positioning scheme named VSpatial in 6G-oriented IoT. VSpatial enables users to verify the correctness and completeness of spatial keyword-based positioning results while preserving user privacy. The main inspiration for addressing the technical challenge is converting both spatial locations and textual keywords into an internal status, i.e., adapting comparison and matching to existence judging by multiple cryptographic tools, such as hierarchical cube and pseudorandom function. Based on this inspiration, we design a novel private authenticated data structure (named PVTree), and then propose two constructions of VSpatial, i.e., VSpatial-S and VSpatial-D, to suit static and dynamic environments, respectively. The core idea for adapting VSpatial-S to VSpatial-D is transferring one whole PVTree into multiple exponential-size partitions. Security analysis proves the security and verifiability of VSpatial. Theoretical and experimental evaluations show that VSpatial achieves faster-than-linear positioning efficiency and linear verification overhead.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2954-2969"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933189","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.2024.3414597
Jingyang Hu;Hongbo Jiang;Siyu Chen;Qibo Zhang;Zhu Xiao;Daibo Liu;Jiangchuan Liu;Bo Li
Wi-Fi signals contain information about the surrounding propagation environment and have been widely used in various sensing applications such as gesture recognition, respiratory monitoring, and indoor position. Nevertheless, this information can also be easily stolen by eavesdroppers to obtain private information. In this paper, we propose WiShield, a new framework that protects legitimate users using Wi-Fi sensing applications while preventing unauthorized privacy attacks. The implementation of WiShield is based on a simple principle of physically encrypting Wi-Fi channel status information (CSI) to prevent eavesdroppers from inferring sensitive information through stolen CSI. To achieve a balance between encryption strength, sensing accuracy, and communication quality, we design an efficient multi-objective optimization framework that can safely deliver decryption keys to legitimate users and prevent illegal eavesdropping by eavesdroppers. We implemented the WiShield prototype on an SDR platform and conducted extensive experiments to verify its effectiveness in common Wi-Fi sensing applications. We believe that the implementation of WiShield can improve the privacy standards of Wi-Fi sensing applications, and it is also an important step towards making the integration of Integrated Sensing and Communications (ISAC).
{"title":"WiShield: Privacy Against Wi-Fi Human Tracking","authors":"Jingyang Hu;Hongbo Jiang;Siyu Chen;Qibo Zhang;Zhu Xiao;Daibo Liu;Jiangchuan Liu;Bo Li","doi":"10.1109/JSAC.2024.3414597","DOIUrl":"10.1109/JSAC.2024.3414597","url":null,"abstract":"Wi-Fi signals contain information about the surrounding propagation environment and have been widely used in various sensing applications such as gesture recognition, respiratory monitoring, and indoor position. Nevertheless, this information can also be easily stolen by eavesdroppers to obtain private information. In this paper, we propose WiShield, a new framework that protects legitimate users using Wi-Fi sensing applications while preventing unauthorized privacy attacks. The implementation of WiShield is based on a simple principle of physically encrypting Wi-Fi channel status information (CSI) to prevent eavesdroppers from inferring sensitive information through stolen CSI. To achieve a balance between encryption strength, sensing accuracy, and communication quality, we design an efficient multi-objective optimization framework that can safely deliver decryption keys to legitimate users and prevent illegal eavesdropping by eavesdroppers. We implemented the WiShield prototype on an SDR platform and conducted extensive experiments to verify its effectiveness in common Wi-Fi sensing applications. We believe that the implementation of WiShield can improve the privacy standards of Wi-Fi sensing applications, and it is also an important step towards making the integration of Integrated Sensing and Communications (ISAC).","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2970-2984"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933102","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.2024.3414580
Jing Bai;Jinsong Gui;Neal N. Xiong;Anfeng Liu;Jie Wu
Mobile Crowd Sensing (MCS), as a promising sensing paradigm, significantly relies on wireless communication networks and widely distributed mobile workers to capture data from the surroundings. However, the positioning-dependent nature of most MCS tasks often requires workers to embed their positionings in reports, which may result in privacy leakage and a decline in their participation enthusiasm. Considering workers’ diverse perceptions of positioning privacy, in this paper we propose the Lightweight Positioning-Privacy Protection Scheme with Double-Layer Incentives (L3P-DLI) to meet their personalized privacy requirements in an efficient and low-cost way while stimulating their participation. To the best of our knowledge, this scheme is the first attempt to employ proxy forwarding to protect workers’ sensitive positionings while ensuring high-quality sensing results. Moreover, our double-layer incentivizing mechanism is elaborately designed to motivate workers to actively participate or serve as proxies. Specifically, the bidirectional auction between data collectors and proxies can safeguard the security of data collectors, and compensate for the potential privacy leakage cost of proxies helping to forward data. Additionally, the reverse auction mechanism enables the platform to reward recruited workers to compensate for their various costs. Extensive experiments conducted on real-world datasets validate that L3P-DLI effectively preserves workers’ positioning privacy while maximizing their income to encourage participation.
{"title":"L3P-DLI: A Lightweight Positioning-Privacy Protection Scheme With Double-Layer Incentives for Wireless Crowd Sensing Systems","authors":"Jing Bai;Jinsong Gui;Neal N. Xiong;Anfeng Liu;Jie Wu","doi":"10.1109/JSAC.2024.3414580","DOIUrl":"10.1109/JSAC.2024.3414580","url":null,"abstract":"Mobile Crowd Sensing (MCS), as a promising sensing paradigm, significantly relies on wireless communication networks and widely distributed mobile workers to capture data from the surroundings. However, the positioning-dependent nature of most MCS tasks often requires workers to embed their positionings in reports, which may result in privacy leakage and a decline in their participation enthusiasm. Considering workers’ diverse perceptions of positioning privacy, in this paper we propose the Lightweight Positioning-Privacy Protection Scheme with Double-Layer Incentives (L3P-DLI) to meet their personalized privacy requirements in an efficient and low-cost way while stimulating their participation. To the best of our knowledge, this scheme is the first attempt to employ proxy forwarding to protect workers’ sensitive positionings while ensuring high-quality sensing results. Moreover, our double-layer incentivizing mechanism is elaborately designed to motivate workers to actively participate or serve as proxies. Specifically, the bidirectional auction between data collectors and proxies can safeguard the security of data collectors, and compensate for the potential privacy leakage cost of proxies helping to forward data. Additionally, the reverse auction mechanism enables the platform to reward recruited workers to compensate for their various costs. Extensive experiments conducted on real-world datasets validate that L3P-DLI effectively preserves workers’ positioning privacy while maximizing their income to encourage participation.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2938-2953"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933171","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.2024.3414589
Peiyue Jiang;Xiaobo Gu;Haibo Zhou
Cooperative positioning in wireless networks has attracted great attention in recent years, as many applications require the exact location of all member nodes. The pairwise distance between the member nodes is conventionally constructed as an Euclidean Distance Matrix (EDM) for subsequent location estimation. In this paper, we address the problem of cooperative positioning in complex propagation environments, which results in an incomplete EDM. We proposed an improved EDM recovery algorithm based on low tank matrix completion (LRMC), which makes use of the sensor correlation by Laplacian and trace minimization. In addition, we derive a semi-definite relaxation estimator to localize the unknown sensors. Simulations are conducted to evaluate the performance of the proposed algorithm and the results show that the proposed method outperforms existing ones in both matrix completion and positioning accuracy.
{"title":"Cooperative Positioning of Wireless Networks in Complex Propagation Environments","authors":"Peiyue Jiang;Xiaobo Gu;Haibo Zhou","doi":"10.1109/JSAC.2024.3414589","DOIUrl":"10.1109/JSAC.2024.3414589","url":null,"abstract":"Cooperative positioning in wireless networks has attracted great attention in recent years, as many applications require the exact location of all member nodes. The pairwise distance between the member nodes is conventionally constructed as an Euclidean Distance Matrix (EDM) for subsequent location estimation. In this paper, we address the problem of cooperative positioning in complex propagation environments, which results in an incomplete EDM. We proposed an improved EDM recovery algorithm based on low tank matrix completion (LRMC), which makes use of the sensor correlation by Laplacian and trace minimization. In addition, we derive a semi-definite relaxation estimator to localize the unknown sensors. Simulations are conducted to evaluate the performance of the proposed algorithm and the results show that the proposed method outperforms existing ones in both matrix completion and positioning accuracy.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2877-2889"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933170","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.2024.3414604
Nguyen Quang Hieu;Dinh Thai Hoang;Diep N. Nguyen;Mohammad Abu Alsheikh
The ability to sense, localize, and estimate the 3D position and orientation of the human body is critical in virtual reality (VR) and extended reality (XR) applications. This becomes more important and challenging with the deployment of VR/XR applications over the next generation of wireless systems such as 5G and beyond. In this paper, we propose a novel framework that can reconstruct the 3D human body pose of the user given sparse measurements from Inertial Measurement Unit (IMU) sensors over a noisy wireless environment. Specifically, our framework enables reliable transmission of compressed IMU signals through noisy wireless channels and effective recovery of such signals at the receiver, e.g., an edge server. This task is very challenging due to the constraints of transmit power, recovery accuracy, and recovery latency. To address these challenges, we first develop a deep generative model at the receiver to recover the data from linear measurements of IMU signals. The linear measurements of the IMU signals are obtained by a linear projection with a measurement matrix based on the compressive sensing theory. The key to the success of our framework lies in the novel design of the measurement matrix at the transmitter, which can not only satisfy power constraints for the IMU devices but also obtain a highly accurate recovery for the IMU signals at the receiver. This can be achieved by extending the set-restricted eigenvalue condition of the measurement matrix and combining it with an upper bound for the power transmission constraint. Our framework can achieve robust performance for recovering 3D human poses from noisy compressed IMU signals. Additionally, our pre-trained deep generative model achieves signal reconstruction accuracy comparable to an optimization-based approach, i.e., Lasso, but is an order of magnitude faster.
{"title":"Reconstructing Human Pose From Inertial Measurements: A Generative Model-Based Compressive Sensing Approach","authors":"Nguyen Quang Hieu;Dinh Thai Hoang;Diep N. Nguyen;Mohammad Abu Alsheikh","doi":"10.1109/JSAC.2024.3414604","DOIUrl":"10.1109/JSAC.2024.3414604","url":null,"abstract":"The ability to sense, localize, and estimate the 3D position and orientation of the human body is critical in virtual reality (VR) and extended reality (XR) applications. This becomes more important and challenging with the deployment of VR/XR applications over the next generation of wireless systems such as 5G and beyond. In this paper, we propose a novel framework that can reconstruct the 3D human body pose of the user given sparse measurements from Inertial Measurement Unit (IMU) sensors over a noisy wireless environment. Specifically, our framework enables reliable transmission of compressed IMU signals through noisy wireless channels and effective recovery of such signals at the receiver, e.g., an edge server. This task is very challenging due to the constraints of transmit power, recovery accuracy, and recovery latency. To address these challenges, we first develop a deep generative model at the receiver to recover the data from linear measurements of IMU signals. The linear measurements of the IMU signals are obtained by a linear projection with a measurement matrix based on the compressive sensing theory. The key to the success of our framework lies in the novel design of the measurement matrix at the transmitter, which can not only satisfy power constraints for the IMU devices but also obtain a highly accurate recovery for the IMU signals at the receiver. This can be achieved by extending the set-restricted eigenvalue condition of the measurement matrix and combining it with an upper bound for the power transmission constraint. Our framework can achieve robust performance for recovering 3D human poses from noisy compressed IMU signals. Additionally, our pre-trained deep generative model achieves signal reconstruction accuracy comparable to an optimization-based approach, i.e., Lasso, but is an order of magnitude faster.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2674-2687"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968806","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.2024.3414932
Muhammad Shohibul Ulum;Uman Khalid;Jason William Setiawan;Trung Q. Duong;Moe Z. Win;Hyundong Shin
QSNs (QSNs) incorporate quantum sensing and quantum communication to achieve Heisenberg precision and unconditional security by leveraging quantum properties such as superposition and entanglement. However, the QSNs deploying noisy intermediate-scale quantum (NISQ) devices face near-term practical challenges. In this paper, we employ variational quantum sensing (VQS) to optimize sensing configurations in noisy environments for the physical quantity of interest, e.g., magnetic-field sensing for navigation, localization, or detection. The VQS algorithm is variationally and evolutionarily optimized using a genetic algorithm for tailoring a variational or parameterized quantum circuit (PQC) structure that effectively mitigates quantum noise effects. This genetic VQS algorithm designs the PQC structure possessing the capability to create a variational probe state that metrologically outperforms the maximally entangled or product quantum state under bit-flip, dephasing, and amplitude-damping quantum noise for both single-parameter and multiparameter NISQ sensing, specifically as quantified by the quantum Fisher information. Furthermore, the quantum anonymous broadcast (QAB) shares the sensing information in the VQS network, ensuring anonymity and untraceability of sensing data. The broadcast bit error probability (BEP) is further analyzed for the QAB protocol under quantum noise, showing its robustness—i.e., error-free resilience—against bit-flip noise as well as the low-noise BEP behavior. This work provides a scalable framework for integrated quantum anonymous sensing and communication, particularly in a variational and untraceable manner.
量子安全网（QSN）结合了量子传感和量子通信，利用叠加和纠缠等量子特性实现海森堡精度和无条件安全。然而，部署噪声中量子（NISQ）器件的 QSNs 面临着近期的实际挑战。在本文中，我们采用变异量子传感（VQS）来优化噪声环境中相关物理量的传感配置，例如用于导航、定位或探测的磁场传感。VQS 算法采用遗传算法进行变异和进化优化，以定制可变或参数化量子电路（PQC）结构，从而有效缓解量子噪声效应。这种遗传 VQS 算法设计的 PQC 结构具有创建变异探测态的能力，在比特翻转、去相和振幅阻尼量子噪声条件下，该探测态的计量性能优于最大纠缠量子态或乘积量子态，适用于单参数和多参数 NISQ 传感，特别是通过量子费雪信息进行量化。此外，量子匿名广播（QAB）在 VQS 网络中共享传感信息，确保了传感数据的匿名性和不可追踪性。我们进一步分析了 QAB 协议在量子噪声下的广播比特错误概率（BEP），显示了它对比特翻转噪声以及低噪声 BEP 行为的鲁棒性（即无差错复原力）。这项工作为集成量子匿名传感和通信提供了一个可扩展的框架，特别是以可变和不可追踪的方式。
{"title":"Variational Anonymous Quantum Sensing","authors":"Muhammad Shohibul Ulum;Uman Khalid;Jason William Setiawan;Trung Q. Duong;Moe Z. Win;Hyundong Shin","doi":"10.1109/JSAC.2024.3414932","DOIUrl":"10.1109/JSAC.2024.3414932","url":null,"abstract":"QSNs (QSNs) incorporate quantum sensing and quantum communication to achieve Heisenberg precision and unconditional security by leveraging quantum properties such as superposition and entanglement. However, the QSNs deploying noisy intermediate-scale quantum (NISQ) devices face near-term practical challenges. In this paper, we employ variational quantum sensing (VQS) to optimize sensing configurations in noisy environments for the physical quantity of interest, e.g., magnetic-field sensing for navigation, localization, or detection. The VQS algorithm is variationally and evolutionarily optimized using a genetic algorithm for tailoring a variational or parameterized quantum circuit (PQC) structure that effectively mitigates quantum noise effects. This genetic VQS algorithm designs the PQC structure possessing the capability to create a variational probe state that metrologically outperforms the maximally entangled or product quantum state under bit-flip, dephasing, and amplitude-damping quantum noise for both single-parameter and multiparameter NISQ sensing, specifically as quantified by the quantum Fisher information. Furthermore, the quantum anonymous broadcast (QAB) shares the sensing information in the VQS network, ensuring anonymity and untraceability of sensing data. The broadcast bit error probability (BEP) is further analyzed for the QAB protocol under quantum noise, showing its robustness—i.e., error-free resilience—against bit-flip noise as well as the low-noise BEP behavior. This work provides a scalable framework for integrated quantum anonymous sensing and communication, particularly in a variational and untraceable manner.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 9","pages":"2275-2291"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933101","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.2024.3414627
Haiying Zhang;Shuyi Chen;Weixiao Meng;Jinhong Yuan;Cheng Li
Supporting multiuser communication and localization is a typical scenario in Integrated sensing and communications (ISAC). However, the problem of multi-echo induced by multipath and multiuser makes it hard to determine the relationship between user equipments (UEs) and these echoes. Thus, applying traditional estimation algorithms at the radar receiver inevitably leads to weak communication and localization performances due to the mismatch between echoes and UEs. In this paper, aiming to achieve multiuser association and localization under doubly dispersive multipath channels, we construct an ISAC unified waveform based on the orthogonal delay-Doppler division multiplexing (ODDM) principle and develop an off-grid cluster sparse Bayesian learning estimation (OG-CSBL) algorithm. Particularly, we focus on the mono-static setup, where the base station (BS) expects to communicate with multiuser while sensing their locations. We utilize the high-resolution range profile (HRRP) to characterize the physical features of UEs and establish associations with their echoes by exploiting the inherent cluster structure. To estimate parameters, we design a hybrid Dirichlet process (DP)-Gaussian hierarchical prior distribution and propose a variational Bayesian inference (VBI)-EM strategy. Additionally, we develop a backtrack echo identification scheme to facilitate precise UE localization. Simulation results demonstrate that the proposed scheme achieves superior NMSE performance, offers meter-level localization accuracy, and obtains better BER performance in the complex multiuser coexistence scenario.
{"title":"Multiuser Association and Localization Over Doubly Dispersive Multipath Channels for Integrated Sensing and Communications","authors":"Haiying Zhang;Shuyi Chen;Weixiao Meng;Jinhong Yuan;Cheng Li","doi":"10.1109/JSAC.2024.3414627","DOIUrl":"10.1109/JSAC.2024.3414627","url":null,"abstract":"Supporting multiuser communication and localization is a typical scenario in Integrated sensing and communications (ISAC). However, the problem of multi-echo induced by multipath and multiuser makes it hard to determine the relationship between user equipments (UEs) and these echoes. Thus, applying traditional estimation algorithms at the radar receiver inevitably leads to weak communication and localization performances due to the mismatch between echoes and UEs. In this paper, aiming to achieve multiuser association and localization under doubly dispersive multipath channels, we construct an ISAC unified waveform based on the orthogonal delay-Doppler division multiplexing (ODDM) principle and develop an off-grid cluster sparse Bayesian learning estimation (OG-CSBL) algorithm. Particularly, we focus on the mono-static setup, where the base station (BS) expects to communicate with multiuser while sensing their locations. We utilize the high-resolution range profile (HRRP) to characterize the physical features of UEs and establish associations with their echoes by exploiting the inherent cluster structure. To estimate parameters, we design a hybrid Dirichlet process (DP)-Gaussian hierarchical prior distribution and propose a variational Bayesian inference (VBI)-EM strategy. Additionally, we develop a backtrack echo identification scheme to facilitate precise UE localization. Simulation results demonstrate that the proposed scheme achieves superior NMSE performance, offers meter-level localization accuracy, and obtains better BER performance in the complex multiuser coexistence scenario.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2847-2862"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933100","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.2024.3414592
Kaixuan Gao;Huiqiang Wang;Hongwu Lv
As a solution for city-range integrated sensing and communication and intelligent positioning, 5G high-precision positioning is flooding into reality. Nevertheless, the underlying positioning security concerns have been overlooked, posing threats to more than a billion emerging 5G localization applications. In this work, we first identify a novel and far-reaching security vulnerability affecting current 5G positioning systems. Correspondingly, we introduce a threat model, called the selective-PRS-spoofing attack (SPS), which can cause substantial localization errors or even fully-hijacked positioning results at victims. The attacker first cracks the broadcast information of a 5G network and then poisons specific resource elements of the channel. Different from traditional communication-oriented 5G attacks, SPS targets the localization and exerts real-world threats. More seriously, we confirm that SPS attacks can evade multiple latest 3GPP R18 defense, and analyze its great stealthiness from its precise spoofing feature. To tackle this challenge, a Deep Learning-based defence method called in-phase quadrature intra-attention network (IQIA-Net) is proposed, which utilizes the hardware features of base stations to perform identification at the physical level, thereby thwarting SPS attacks on 5G positioning systems. Extensive experiments demonstrate the effectiveness of our method and its good robustness to noise.
{"title":"Surgical Strike on 5G Positioning: Selective-PRS-Spoofing Attacks and Its Defence","authors":"Kaixuan Gao;Huiqiang Wang;Hongwu Lv","doi":"10.1109/JSAC.2024.3414592","DOIUrl":"10.1109/JSAC.2024.3414592","url":null,"abstract":"As a solution for city-range integrated sensing and communication and intelligent positioning, 5G high-precision positioning is flooding into reality. Nevertheless, the underlying positioning security concerns have been overlooked, posing threats to more than a billion emerging 5G localization applications. In this work, we first identify a novel and far-reaching security vulnerability affecting current 5G positioning systems. Correspondingly, we introduce a threat model, called the selective-PRS-spoofing attack (SPS), which can cause substantial localization errors or even fully-hijacked positioning results at victims. The attacker first cracks the broadcast information of a 5G network and then poisons specific resource elements of the channel. Different from traditional communication-oriented 5G attacks, SPS targets the localization and exerts real-world threats. More seriously, we confirm that SPS attacks can evade multiple latest 3GPP R18 defense, and analyze its great stealthiness from its precise spoofing feature. To tackle this challenge, a Deep Learning-based defence method called in-phase quadrature intra-attention network (IQIA-Net) is proposed, which utilizes the hardware features of base stations to perform identification at the physical level, thereby thwarting SPS attacks on 5G positioning systems. Extensive experiments demonstrate the effectiveness of our method and its good robustness to noise.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2922-2937"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933185","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.2024.3414626
Zhixiang Hu;An Liu;Wenkang Xu;Tony Q. S. Quek;Minjian Zhao
Future wireless networks are envisioned to provide ubiquitous sensing services, driving a substantial demand for multi-dimensional non-convex parameter estimation. This entails dealing with non-convex likelihood functions containing numerous local optima. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estimation problems and leveraging prior information, but poses a long-standing challenge on computing intractable posterior distributions. Most existing variational methods depend on specific distribution assumptions for obtaining closed-form solutions, and are difficult to apply in practical scenarios. Given these challenges, firstly, we propose a parallel stochastic particle VBI (PSPVBI) algorithm. Due to innovations like particle approximation, added updates of particle positions, and parallel stochastic successive convex approximation (PSSCA), PSPVBI can flexibly drive particles to fit the posterior distribution with acceptable complexity, yielding high-precision estimates of the target parameters. Furthermore, additional speedup can be obtained by deep-unfolding this algorithm. Specifically, superior hyperparameters are learned to dramatically reduce iterations. In this PSPVBI-induced deep-unfolding network, some techniques related to gradient computation, data sub-sampling, differentiable sampling, and generalization ability are also employed to facilitate the practical deployment. Finally, we apply the learnable PSPVBI (LPSPVBI) to solve two important positioning/sensing problems over wireless networks. Simulations indicate that the LPSPVBI algorithm outperforms existing solutions.
{"title":"A Stochastic Particle Variational Bayesian Inference Inspired Deep-Unfolding Network for Sensing Over Wireless Networks","authors":"Zhixiang Hu;An Liu;Wenkang Xu;Tony Q. S. Quek;Minjian Zhao","doi":"10.1109/JSAC.2024.3414626","DOIUrl":"10.1109/JSAC.2024.3414626","url":null,"abstract":"Future wireless networks are envisioned to provide ubiquitous sensing services, driving a substantial demand for multi-dimensional non-convex parameter estimation. This entails dealing with non-convex likelihood functions containing numerous local optima. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estimation problems and leveraging prior information, but poses a long-standing challenge on computing intractable posterior distributions. Most existing variational methods depend on specific distribution assumptions for obtaining closed-form solutions, and are difficult to apply in practical scenarios. Given these challenges, firstly, we propose a parallel stochastic particle VBI (PSPVBI) algorithm. Due to innovations like particle approximation, added updates of particle positions, and parallel stochastic successive convex approximation (PSSCA), PSPVBI can flexibly drive particles to fit the posterior distribution with acceptable complexity, yielding high-precision estimates of the target parameters. Furthermore, additional speedup can be obtained by deep-unfolding this algorithm. Specifically, superior hyperparameters are learned to dramatically reduce iterations. In this PSPVBI-induced deep-unfolding network, some techniques related to gradient computation, data sub-sampling, differentiable sampling, and generalization ability are also employed to facilitate the practical deployment. Finally, we apply the learnable PSPVBI (LPSPVBI) to solve two important positioning/sensing problems over wireless networks. Simulations indicate that the LPSPVBI algorithm outperforms existing solutions.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"42 10","pages":"2832-2846"},"PeriodicalIF":0.0,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141933123","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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