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":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":"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":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":"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.2024.3414604
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":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":"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.2024.3414592
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":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":"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":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":"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}
Pub Date : 2024-06-14DOI: 10.1109/JSAC.2024.3414599
Ran Wang;Cheng Xu;Jing Sun;Shihong Duan;Xiaotong Zhang
In modern navigation and positioning systems, accurate location information is crucial for ensuring system performance and user experience. Particularly, in scenarios involving the use of multiple agents such as robots and drones for rescue operations in unknown complex environments, accurate localization is fundamental for subsequent actions. However, traditional filtering-based localization algorithms may exhibit suboptimal performance and are sensitive to initial estimates and system noise. To address these issues, this paper proposes a multi-agent collaborative localization algorithm based on reinforcement learning compensation filtering to tackle localization problems in complex environments and improve the robustness and accuracy. Specifically, this paper introduces a value decomposition-based reinforcement learning network for filtering compensation to reduce overall localization error and address the credit allocation problem in multi-agent reinforcement learning. The main contributions of this paper are as follows: Firstly, a local localization estimation method based on reinforcement learning compensation Extended Kalman Filter (EKF) is proposed, which further corrects the results of the EKF algorithm and eliminates initial estimation errors. Secondly, a global collaborative localization estimation algorithm (MARL_CF) based on credit allocation in multi-agent reinforcement learning is proposed, which maximizes the reduction of overall localization error through information sharing and global optimization. Finally, the effectiveness of the proposed algorithms is validated through both numerical simulation and physical experiments. The results demonstrate that the proposed MARL_CF significantly improve the accuracy and robustness of localization in complex environments.
{"title":"Cooperative Localization for Multi-Agents Based on Reinforcement Learning Compensated Filter","authors":"Ran Wang;Cheng Xu;Jing Sun;Shihong Duan;Xiaotong Zhang","doi":"10.1109/JSAC.2024.3414599","DOIUrl":"10.1109/JSAC.2024.3414599","url":null,"abstract":"In modern navigation and positioning systems, accurate location information is crucial for ensuring system performance and user experience. Particularly, in scenarios involving the use of multiple agents such as robots and drones for rescue operations in unknown complex environments, accurate localization is fundamental for subsequent actions. However, traditional filtering-based localization algorithms may exhibit suboptimal performance and are sensitive to initial estimates and system noise. To address these issues, this paper proposes a multi-agent collaborative localization algorithm based on reinforcement learning compensation filtering to tackle localization problems in complex environments and improve the robustness and accuracy. Specifically, this paper introduces a value decomposition-based reinforcement learning network for filtering compensation to reduce overall localization error and address the credit allocation problem in multi-agent reinforcement learning. The main contributions of this paper are as follows: Firstly, a local localization estimation method based on reinforcement learning compensation Extended Kalman Filter (EKF) is proposed, which further corrects the results of the EKF algorithm and eliminates initial estimation errors. Secondly, a global collaborative localization estimation algorithm (MARL_CF) based on credit allocation in multi-agent reinforcement learning is proposed, which maximizes the reduction of overall localization error through information sharing and global optimization. Finally, the effectiveness of the proposed algorithms is validated through both numerical simulation and physical experiments. The results demonstrate that the proposed MARL_CF significantly improve the accuracy and robustness of localization in complex environments.","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":"141933190","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.2024.3413997
Guoxuan Chi;Guidong Zhang;Xuan Ding;Qiang Ma;Zheng Yang;Zhenguo Du;Houfei Xiao;Zhuang Liu
Recent years have witnessed an increasing demand for human fall detection systems. Among all existing methods, Wi-Fi-based fall detection has become one of the most promising solutions due to its pervasiveness. However, when applied to a new domain, existing Wi-Fi-based solutions suffer from severe performance degradation caused by low generalizability. In this paper, we propose XFall, a domain-adaptive fall detection system based on Wi-Fi. XFall overcomes the generalization problem from three aspects. To advance cross-environment sensing, XFall exploits an environment-independent feature called speed distribution profile, which is irrelevant to indoor layout and device deployment. To ensure sensitivity across all fall types, an attention-based encoder is designed to extract the general fall representation by associating both the spatial and temporal dimensions of the input. To train a large model with limited amounts of Wi-Fi data, we design a cross-modal learning framework, adopting a pre-trained visual model for supervision during the training process. We implement and evaluate XFall on one of the latest commercial wireless products through a year-long deployment in real-world settings. The result shows XFall achieves an overall accuracy of 96.8%, with a miss alarm rate of 3.1% and a false alarm rate of 3.3%, outperforming the state-of-the-art solutions in both in-domain and cross-domain evaluation.
{"title":"XFall: Domain Adaptive Wi-Fi-Based Fall Detection With Cross-Modal Supervision","authors":"Guoxuan Chi;Guidong Zhang;Xuan Ding;Qiang Ma;Zheng Yang;Zhenguo Du;Houfei Xiao;Zhuang Liu","doi":"10.1109/JSAC.2024.3413997","DOIUrl":"10.1109/JSAC.2024.3413997","url":null,"abstract":"Recent years have witnessed an increasing demand for human fall detection systems. Among all existing methods, Wi-Fi-based fall detection has become one of the most promising solutions due to its pervasiveness. However, when applied to a new domain, existing Wi-Fi-based solutions suffer from severe performance degradation caused by low generalizability. In this paper, we propose XFall, a domain-adaptive fall detection system based on Wi-Fi. XFall overcomes the generalization problem from three aspects. To advance cross-environment sensing, XFall exploits an environment-independent feature called speed distribution profile, which is irrelevant to indoor layout and device deployment. To ensure sensitivity across all fall types, an attention-based encoder is designed to extract the general fall representation by associating both the spatial and temporal dimensions of the input. To train a large model with limited amounts of Wi-Fi data, we design a cross-modal learning framework, adopting a pre-trained visual model for supervision during the training process. We implement and evaluate XFall on one of the latest commercial wireless products through a year-long deployment in real-world settings. The result shows XFall achieves an overall accuracy of 96.8%, with a miss alarm rate of 3.1% and a false alarm rate of 3.3%, outperforming the state-of-the-art solutions in both in-domain and cross-domain evaluation.","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":"141933122","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.2024.3414606
Zhuyin Li;Xu Zhu;Jie Cao
In the realm of industrial Internet of Things (IIoT), the concept of location awareness plays a crucial role in the integrated sensing and communication (ISAC) framework. This paper introduces an innovative methodology for assessing the location awareness of a mobile entity by combining the precision of the positioning algorithm and the timeliness of location estimations based on the age of information (AoI). The assessment employs a novel metric termed as the aging error of localization (AEoL), which encapsulates both the accuracy of localization and its evolution over the data packet lifecycle. This metric bridges a gap in existing research, which predominantly emphasizes geographical precision while neglecting the dynamic spatial attributes of a mobile entity, thereby offering valuable insights into both the precision and temporal aspects of location awareness. The study delves into the evaluation of AEoL under scenarios of perfect and imperfect localization algorithm precision. By considering a scenario where an automated guided vehicle (AGV) adheres to the uniform rectilinear motion (URM) and transmits radio signals via specific queuing models, analytical expressions for the time-average AEoL are derived across varying update rates. These expressions are subsequently validated through numerical simulations. Furthermore, for specific root mean square error (RMSE) scenarios, optimal update rates are recommended, through which the performance of location awareness can be enhanced by reducing the AEoL metric by 10% to 68% compared to the worst-case scenario.
{"title":"Enhancing Location Awareness: A Perspective on Age of Information and Localization Precision","authors":"Zhuyin Li;Xu Zhu;Jie Cao","doi":"10.1109/JSAC.2024.3414606","DOIUrl":"10.1109/JSAC.2024.3414606","url":null,"abstract":"In the realm of industrial Internet of Things (IIoT), the concept of location awareness plays a crucial role in the integrated sensing and communication (ISAC) framework. This paper introduces an innovative methodology for assessing the location awareness of a mobile entity by combining the precision of the positioning algorithm and the timeliness of location estimations based on the age of information (AoI). The assessment employs a novel metric termed as the aging error of localization (AEoL), which encapsulates both the accuracy of localization and its evolution over the data packet lifecycle. This metric bridges a gap in existing research, which predominantly emphasizes geographical precision while neglecting the dynamic spatial attributes of a mobile entity, thereby offering valuable insights into both the precision and temporal aspects of location awareness. The study delves into the evaluation of AEoL under scenarios of perfect and imperfect localization algorithm precision. By considering a scenario where an automated guided vehicle (AGV) adheres to the uniform rectilinear motion (URM) and transmits radio signals via specific queuing models, analytical expressions for the time-average AEoL are derived across varying update rates. These expressions are subsequently validated through numerical simulations. Furthermore, for specific root mean square error (RMSE) scenarios, optimal update rates are recommended, through which the performance of location awareness can be enhanced by reducing the AEoL metric by 10% to 68% compared to the worst-case scenario.","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":"141933103","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.2024.3414608
Haoming Yang;Qiran Zhao;Hao Wang;Chi Harold Liu;Guozheng Li;Guoren Wang;Jian Tang;Dapeng Wu
Indoor localization is drawing more and more attentions due to the growing demand of various location-based services, where fingerprinting is a popular data driven techniques that does not rely on complex measurement equipment, yet it requires site surveys which is both labor-intensive and time-consuming. Vehicular crowdsensing (VCS) with unmanned vehicles (UVs) is a novel paradigm to navigate a group of UVs to collect sensory data from certain point-of-interests periodically (PoIs, i.e., coverage holes in localization scenarios). In this paper, we formulate the multi-floor indoor fingerprint collection task with periodical PoI coverage requirements as a constrained optimization problem. Then, we propose a multi-agent deep reinforcement learning (MADRL) based solution, “MADRL-PosVCS”, which consists of a primal-dual framework to transform the above optimization problem into the unconstrained duality, with adjustable Lagrangian multipliers to ensure periodic fingerprint collection. We also propose a novel intrinsic reward mechanism consists of the mutual information between a UV’s observations and environment transition probability parameterized by a Bayesian Neural Network (BNN) for exploration, and a elevator-based reward to allow UVs to go cross different floors for collaborative fingerprint collections. Extensive simulation results on three real-world datasets in SML Center (Shanghai), Joy City (Hangzhou) and Haopu Fashion City (Shanghai) show that MADRL-PosVCS achieves better results over four baselines on fingerprint collection ratio, PoI coverage ratio for collection intervals, geographic fairness and average moving distance.
由于各种基于位置的服务的需求日益增长，室内定位越来越受到关注，其中指纹识别是一种流行的数据驱动技术，它不依赖于复杂的测量设备，但它需要现场勘测，既耗费人力又耗费时间。使用无人车（UVs）的车载群感（VCS）是一种新颖的范例，它可以引导一群无人车定期从某些兴趣点（PoIs，即定位场景中的覆盖孔）收集感知数据。在本文中，我们将具有周期性 PoI 覆盖要求的多楼层室内指纹采集任务表述为一个约束优化问题。然后，我们提出了一种基于多代理深度强化学习（MADRL）的解决方案--"MADRL-PosVCS"，它包括一个将上述优化问题转化为无约束二元性的基元-二元框架，以及可调整的拉格朗日乘数，以确保周期性指纹采集。我们还提出了一种新颖的内在奖励机制，包括由贝叶斯神经网络（BNN）参数化的 UV 观察结果与环境转换概率之间的互信息，用于探索；以及基于电梯的奖励，允许 UV 穿过不同楼层，协同采集指纹。在上海 SML 中心、杭州大悦城和豪普时尚城三个真实世界数据集上进行的大量仿真结果表明，MADRL-PosVCS 在指纹采集率、采集间隔的 PoI 覆盖率、地理公平性和平均移动距离方面都比四种基线方法取得了更好的结果。
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Pub Date : 2024-06-14DOI: 10.1109/JSAC.2024.3414619
Jingyi Ning;Lei Xie;Yi Li;Yingying Chen;Yanling Bu;Chuyu Wang;Sanglu Lu;Baoliu Ye
In the realm of AR applications and particularly camera-to-screen interactions, camera tracking stands as a crucial technology. However, the ever-increasing demand for tracking accuracy makes it essential to explore a six-degrees of freedom (6-DoF) tracking technology with ultra-high precision to facilitate micro-motion sensing. In this paper, we propose a novel sensing method MoiréTracker to achieve camera’s 6-DoF pose tracking with ultra-high precision. MoiréTracker outputs camera’s continuous 3-DoF trajectory and 3-DoF posture changes according to the captured moiré patterns, which can be produced by the superposition of camera’s Color Filter Array (CFA) and the projection of screen raster on the CFA plane. Thanks to moiré pattern’s high sensitivity to 6-DoF motions, we characterize the relationship between moiré features and camera’s micro pose changes, so as to realize the continuous 6-DoF pose tracking for camera with ultra-high precision. Moreover, our proposal involves a thumbnail-based method aimed at expanding the working range of MoiréTracker, enabling the pervasive camera-to-screen interactions. We implement a prototype system and evaluate its performance in real-world environments. Extensive experiment results show that MoiréTracker achieves the average trajectory error of 1.20 cm and the posture error of 1.07°.
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