Pub Date : 2025-09-18DOI: 10.1109/JSAC.2025.3611404
Milad Tatar Mamaghani;Xiangyun Zhou;Nan Yang;A. Lee Swindlehurst
In this paper, we study a secure integrated sensing and communication (ISAC) system employing a full-duplex base station with sensing capabilities against a mobile proactive adversarial target—a malicious uncrewed aerial vehicle (M-UAV). We develop a game-theoretic model to enhance communication security, radar sensing accuracy, and power efficiency. The interaction between the legitimate network and the mobile adversary is formulated as a non-cooperative Stackelberg game (NSG), where the M-UAV acts as the leader and strategically adjusts its trajectory to improve its eavesdropping ability while conserving power and avoiding obstacles. In response, the legitimate network, acting as the follower, dynamically allocates resources to minimize network power usage while ensuring required secrecy rates and sensing performance. To address this challenging problem, we propose a low-complexity successive convex approximation (SCA) method for network resource optimization combined with a deep reinforcement learning (DRL) algorithm for adaptive M-UAV trajectory planning through sequential interactions and learning. Simulation results demonstrate the efficacy of the proposed method in addressing security challenges of dynamic ISAC systems in 6G, i.e., achieving a Stackelberg equilibrium with robust performance while mitigating the adversary’s ability to intercept network signals.
{"title":"Securing Integrated Sensing and Communication Against a Mobile Adversary: A Stackelberg Game With Deep Reinforcement Learning","authors":"Milad Tatar Mamaghani;Xiangyun Zhou;Nan Yang;A. Lee Swindlehurst","doi":"10.1109/JSAC.2025.3611404","DOIUrl":"10.1109/JSAC.2025.3611404","url":null,"abstract":"In this paper, we study a secure integrated sensing and communication (ISAC) system employing a full-duplex base station with sensing capabilities against a mobile proactive adversarial target—a malicious uncrewed aerial vehicle (M-UAV). We develop a game-theoretic model to enhance communication security, radar sensing accuracy, and power efficiency. The interaction between the legitimate network and the mobile adversary is formulated as a non-cooperative Stackelberg game (NSG), where the M-UAV acts as the leader and strategically adjusts its trajectory to improve its eavesdropping ability while conserving power and avoiding obstacles. In response, the legitimate network, acting as the follower, dynamically allocates resources to minimize network power usage while ensuring required secrecy rates and sensing performance. To address this challenging problem, we propose a low-complexity successive convex approximation (SCA) method for network resource optimization combined with a deep reinforcement learning (DRL) algorithm for adaptive M-UAV trajectory planning through sequential interactions and learning. Simulation results demonstrate the efficacy of the proposed method in addressing security challenges of dynamic ISAC systems in 6G, i.e., achieving a Stackelberg equilibrium with robust performance while mitigating the adversary’s ability to intercept network signals.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"942-958"},"PeriodicalIF":17.2,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145083677","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 : 2025-09-17DOI: 10.1109/JSAC.2025.3610806
Xuanhao Luo;Zhouyu Li;Mingzhe Chen;Ruozhou Yu;Shiwen Mao;Yuchen Liu
Integrated sensing and communication systems face critical challenges, including limited bandwidth, power constraints, and varying communication conditions, which demand efficient data transmission and processing strategies. This paper introduces, ByteTrans, a novel joint optimization framework that integrates byte-level predictive modeling with adaptive model scheduling to maximize data transmission efficiency while adhering to communication and computational constraints. The proposed framework employs Transformer-based models to predict and compress data packets losslessly, leveraging the inherent redundancy in multi-modal network data. Such a unified data compression approach predicts occurring byte probabilities, encodes them as ranks using lossless entropy coding, and efficiently reduces data size and entropy across diverse modalities. Then, a dynamic adaptation strategy selects the optimal compression model based on packet characteristics and channel conditions, ensuring efficient operation across heterogeneous sensor environments. Experimental results validate that our scheme achieves compression rates exceeding 50%, while showcasing substantial reductions in communication time and bandwidth usage under both normal and adverse channel conditions. Furthermore, we effectively implement these models across various real-world edge sensors and servers, showcasing their practicality and efficiency in various network applications. By addressing the trade-offs between achieving lower compression ratios and limiting computational and energy consumption, this work establishes a scalable and robust solution for data management in multi-modal communication systems.
{"title":"Unified Packet Compression and Model Adaptation for Integrated Sensing and Multi-Modal Communications","authors":"Xuanhao Luo;Zhouyu Li;Mingzhe Chen;Ruozhou Yu;Shiwen Mao;Yuchen Liu","doi":"10.1109/JSAC.2025.3610806","DOIUrl":"10.1109/JSAC.2025.3610806","url":null,"abstract":"Integrated sensing and communication systems face critical challenges, including limited bandwidth, power constraints, and varying communication conditions, which demand efficient data transmission and processing strategies. This paper introduces, ByteTrans, a novel joint optimization framework that integrates byte-level predictive modeling with adaptive model scheduling to maximize data transmission efficiency while adhering to communication and computational constraints. The proposed framework employs Transformer-based models to predict and compress data packets losslessly, leveraging the inherent redundancy in multi-modal network data. Such a unified data compression approach predicts occurring byte probabilities, encodes them as ranks using lossless entropy coding, and efficiently reduces data size and entropy across diverse modalities. Then, a dynamic adaptation strategy selects the optimal compression model based on packet characteristics and channel conditions, ensuring efficient operation across heterogeneous sensor environments. Experimental results validate that our scheme achieves compression rates exceeding 50%, while showcasing substantial reductions in communication time and bandwidth usage under both normal and adverse channel conditions. Furthermore, we effectively implement these models across various real-world edge sensors and servers, showcasing their practicality and efficiency in various network applications. By addressing the trade-offs between achieving lower compression ratios and limiting computational and energy consumption, this work establishes a scalable and robust solution for data management in multi-modal communication systems.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"913-926"},"PeriodicalIF":17.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145077599","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 Communications (ISAC) integrates sensing and communication functions through ubiquitous wireless signals, providing a seamless and flexible interaction experience, making it an ideal choice for intelligent Human-Computer Interaction (HCI). Among various interaction methods, gesture recognition has garnered widespread attention. However, current RF-based gesture recognition methods within ISAC are constrained by single-target sensing and insufficient robustness. In this paper, we propose MultiGes, a real-time multi-user gesture recognition system designed to support ISAC-driven scenarios. MultiGes employs dual commercial Impulse Radio Ultra-Wideband (IR-UWB) devices to create multiple wireless links, capturing dynamic motion features from multiple targets. First, a human energy map is constructed based on the reflected signal energy to determine multi-target coordinates. Then, a Differential Human Relative Velocity (DHRV) matrix is extracted to capture fine-grained motion information. Finally, we design a lightweight STNet model to extract spatiotemporal gesture features from the DHRV matrix, enabling real-time multi-target gesture recognition. We implement the MultiGes system prototype and conduct extensive experiments on ten common gestures in HCI scenarios. Experimental results demonstrate that MultiGes achieves efficient recognition for 2 to 5 users, with an average accuracy of over 90%, providing a robust, scalable, and real-time solution for multi-target gesture recognition in ISAC-driven smart environments.
{"title":"MultiGes: Real-Time Multi-Target Gesture Recognition for ISAC-Driven Human–Computer Interaction","authors":"Zhengxin Guo;Dongzi Wang;Kaiyan Cui;Linqing Gui;Ning Ye;Fu Xiao","doi":"10.1109/JSAC.2025.3609762","DOIUrl":"10.1109/JSAC.2025.3609762","url":null,"abstract":"Integrated Sensing and Communications (ISAC) integrates sensing and communication functions through ubiquitous wireless signals, providing a seamless and flexible interaction experience, making it an ideal choice for intelligent Human-Computer Interaction (HCI). Among various interaction methods, gesture recognition has garnered widespread attention. However, current RF-based gesture recognition methods within ISAC are constrained by single-target sensing and insufficient robustness. In this paper, we propose MultiGes, a real-time multi-user gesture recognition system designed to support ISAC-driven scenarios. MultiGes employs dual commercial Impulse Radio Ultra-Wideband (IR-UWB) devices to create multiple wireless links, capturing dynamic motion features from multiple targets. First, a human energy map is constructed based on the reflected signal energy to determine multi-target coordinates. Then, a Differential Human Relative Velocity (DHRV) matrix is extracted to capture fine-grained motion information. Finally, we design a lightweight STNet model to extract spatiotemporal gesture features from the DHRV matrix, enabling real-time multi-target gesture recognition. We implement the MultiGes system prototype and conduct extensive experiments on ten common gestures in HCI scenarios. Experimental results demonstrate that MultiGes achieves efficient recognition for 2 to 5 users, with an average accuracy of over 90%, providing a robust, scalable, and real-time solution for multi-target gesture recognition in ISAC-driven smart environments.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"899-912"},"PeriodicalIF":17.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072675","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}
Alongside the ongoing standardization efforts for WiFi sensing, WiFi has emerged as a leading technology for Integrated Sensing and Communications (ISAC) with numerous sensing applications demonstrating its significant potentials. Material and size sensing, essential in quality control and digital twins, has drawn much interest. Yet, simultaneous material and 3D size sensing remains challenging, primarily due to the lack of suitable sensing models for objects at near-wavelength scales. This paper introduces WiCaliper, the first WiFi-based system addressing this problem. Its core innovation is DP-CSI, a novel sensing model that captures both diffraction and penetration effects to characterize the relationship between channel state information and the material, shape, and size of everyday 3D objects. To effectively solve for multiple object parameters, WiCaliper employs a two-step estimation process consisting of closed-form property function recovery and multi-view joint parameter optimization. Experimental evaluations show that it achieves 95% material classification accuracy and a 1.5 cm median error in 3D size sensing. This work advances ISAC theory by establishing principles for wavelength-scale 3D object sensing, paving the way for new sensing applications.
{"title":"WiCaliper: Simultaneous Material and 3D Size Sensing for Everyday Objects Using WiFi","authors":"Zhiyun Yao;Kai Niu;Xuanzhi Wang;Rong Zheng;Junzhe Wang;Duo Zhang;Daqing Zhang","doi":"10.1109/JSAC.2025.3609312","DOIUrl":"10.1109/JSAC.2025.3609312","url":null,"abstract":"Alongside the ongoing standardization efforts for WiFi sensing, WiFi has emerged as a leading technology for Integrated Sensing and Communications (ISAC) with numerous sensing applications demonstrating its significant potentials. Material and size sensing, essential in quality control and digital twins, has drawn much interest. Yet, simultaneous material and 3D size sensing remains challenging, primarily due to the lack of suitable sensing models for objects at near-wavelength scales. This paper introduces WiCaliper, the first WiFi-based system addressing this problem. Its core innovation is DP-CSI, a novel sensing model that captures both diffraction and penetration effects to characterize the relationship between channel state information and the material, shape, and size of everyday 3D objects. To effectively solve for multiple object parameters, WiCaliper employs a two-step estimation process consisting of closed-form property function recovery and multi-view joint parameter optimization. Experimental evaluations show that it achieves 95% material classification accuracy and a 1.5 cm median error in 3D size sensing. This work advances ISAC theory by establishing principles for wavelength-scale 3D object sensing, paving the way for new sensing applications.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"883-898"},"PeriodicalIF":17.2,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145072679","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 : 2025-09-11DOI: 10.1109/JSAC.2025.3608770
Chen Fang;Chi Harold Liu;Hao Wang;Guangpeng Qi;Zhongyi Liu;Dapeng Wu
Integrated sensing and communication (ISAC) has emerged as a transformative paradigm, merging the capabilities of sensing and communication to enhance efficiency and enable advanced applications. Mobile crowdsensing (MCS), as a important example of ISAC, leverages unmanned vehicles such as UAVs to continuously gather and transmit environmental data, supporting critical applications like traffic monitoring, urban congestion management, and accident investigation. In this paper, we focus on multi-task-oriented UAV crowdsensing (UCS), where diverse tasks—such as surveillance and emergency response—each have distinct age-of-information (AoI) requirements. We introduce a novel metric, the “valid task handling index,” to evaluate the performance of handling multiple tasks effectively. Our proposed hierarchical multi-agent deep reinforcement learning (MADRL) framework, DRL-MTUCS, integrates seamlessly with multi-agent actor-critic reinforcement learning methods. It features dynamically weighted queues for UAV goal assignment, enabling efficient management of multiple emergency tasks, and a low-level UAV execution module with a self-balancing intrinsic reward mechanism. This ensures all tasks are completed within their individual AoI constraints. Extensive experiments and trajectory visualizations validate the superior performance and robustness of DRL-MTUCS compared to six baselines across varying conditions, including the number of UAVs, surveillance task AoI thresholds, and emergency task image blur requirements.
{"title":"Multi-Task-Oriented Emergency-Aware UAV Crowdsensing: A Hierarchical Multi-Agent Deep Reinforcement Learning Approach","authors":"Chen Fang;Chi Harold Liu;Hao Wang;Guangpeng Qi;Zhongyi Liu;Dapeng Wu","doi":"10.1109/JSAC.2025.3608770","DOIUrl":"10.1109/JSAC.2025.3608770","url":null,"abstract":"Integrated sensing and communication (ISAC) has emerged as a transformative paradigm, merging the capabilities of sensing and communication to enhance efficiency and enable advanced applications. Mobile crowdsensing (MCS), as a important example of ISAC, leverages unmanned vehicles such as UAVs to continuously gather and transmit environmental data, supporting critical applications like traffic monitoring, urban congestion management, and accident investigation. In this paper, we focus on multi-task-oriented UAV crowdsensing (UCS), where diverse tasks—such as surveillance and emergency response—each have distinct age-of-information (AoI) requirements. We introduce a novel metric, the “valid task handling index,” to evaluate the performance of handling multiple tasks effectively. Our proposed hierarchical multi-agent deep reinforcement learning (MADRL) framework, DRL-MTUCS, integrates seamlessly with multi-agent actor-critic reinforcement learning methods. It features dynamically weighted queues for UAV goal assignment, enabling efficient management of multiple emergency tasks, and a low-level UAV execution module with a self-balancing intrinsic reward mechanism. This ensures all tasks are completed within their individual AoI constraints. Extensive experiments and trajectory visualizations validate the superior performance and robustness of DRL-MTUCS compared to six baselines across varying conditions, including the number of UAVs, surveillance task AoI thresholds, and emergency task image blur requirements.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"866-882"},"PeriodicalIF":17.2,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145035450","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 : 2025-09-09DOI: 10.1109/JSAC.2025.3602255
{"title":"IEEE Journal on Selected Areas in Communications Publication Information","authors":"","doi":"10.1109/JSAC.2025.3602255","DOIUrl":"https://doi.org/10.1109/JSAC.2025.3602255","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 9","pages":"C2-C2"},"PeriodicalIF":17.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11154651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036873","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 : 2025-09-09DOI: 10.1109/JSAC.2025.3602257
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/JSAC.2025.3602257","DOIUrl":"https://doi.org/10.1109/JSAC.2025.3602257","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 9","pages":"C3-C3"},"PeriodicalIF":17.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11154836","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021296","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 : 2025-09-05DOI: 10.1109/JSAC.2025.3567101
David S. L. Wei;Kaiping Xue;Tao Zhang;David Elkous;Lidong Chen;Carlo Ottaviani
{"title":"Guest Editorial: Building a More Secure Future: Developing Unbreakable Communication Protocols for the Quantum Era","authors":"David S. L. Wei;Kaiping Xue;Tao Zhang;David Elkous;Lidong Chen;Carlo Ottaviani","doi":"10.1109/JSAC.2025.3567101","DOIUrl":"https://doi.org/10.1109/JSAC.2025.3567101","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 8","pages":"2728-2731"},"PeriodicalIF":17.2,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151738","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998192","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 : 2025-09-04DOI: 10.1109/JSAC.2025.3579193
{"title":"IEEE Journal on Selected Areas in Communications Publication Information","authors":"","doi":"10.1109/JSAC.2025.3579193","DOIUrl":"https://doi.org/10.1109/JSAC.2025.3579193","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 8","pages":"C2-C2"},"PeriodicalIF":17.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151741","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990071","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 : 2025-09-04DOI: 10.1109/JSAC.2025.3579195
{"title":"IEEE Communications Society Information","authors":"","doi":"10.1109/JSAC.2025.3579195","DOIUrl":"https://doi.org/10.1109/JSAC.2025.3579195","url":null,"abstract":"","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 8","pages":"C3-C3"},"PeriodicalIF":17.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11151731","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144990261","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}
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