Pub Date : 2025-09-24DOI: 10.1109/JSAC.2025.3613672
Jiacheng Wang;Changyuan Zhao;Hongyang Du;Geng Sun;Jiawen Kang;Shiwen Mao;Dusit Niyato;Dong In Kim
Integrated sensing and communication (ISAC) uses the same software and hardware resources to achieve both communication and sensing functionalities. Thus, it stands as one of the core technologies of 6G and has garnered significant attention in recent years. In ISAC systems, a variety of machine learning models are trained to analyze and identify signal patterns, thereby ensuring reliable sensing and communications. However, considering factors such as communication rates, costs, and privacy, collecting sufficient training data from various ISAC scenarios for these models is impractical. Hence, this paper introduces a generative AI (GenAI) enabled robust data augmentation scheme. The scheme first employs a conditioned diffusion model trained on a limited amount of collected CSI data to generate new samples, thereby enhancing the sample quantity. Building on this, the scheme further utilizes another diffusion model to enhance the sample quality, thereby facilitating the data augmentation in scenarios where the original sensing data is insufficient and unevenly distributed. Moreover, we propose a novel algorithm to estimate the acceleration and jerk of signal propagation path length changes from CSI. We then use the proposed scheme to enhance the estimated parameters and detect the number of targets based on the enhanced data. The evaluation reveals that our scheme improves the detection performance by up to 70%, demonstrating reliability and robustness, which supports the deployment and practical use of the ISAC network.
{"title":"Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks","authors":"Jiacheng Wang;Changyuan Zhao;Hongyang Du;Geng Sun;Jiawen Kang;Shiwen Mao;Dusit Niyato;Dong In Kim","doi":"10.1109/JSAC.2025.3613672","DOIUrl":"10.1109/JSAC.2025.3613672","url":null,"abstract":"Integrated sensing and communication (ISAC) uses the same software and hardware resources to achieve both communication and sensing functionalities. Thus, it stands as one of the core technologies of 6G and has garnered significant attention in recent years. In ISAC systems, a variety of machine learning models are trained to analyze and identify signal patterns, thereby ensuring reliable sensing and communications. However, considering factors such as communication rates, costs, and privacy, collecting sufficient training data from various ISAC scenarios for these models is impractical. Hence, this paper introduces a generative AI (GenAI) enabled robust data augmentation scheme. The scheme first employs a conditioned diffusion model trained on a limited amount of collected CSI data to generate new samples, thereby enhancing the sample quantity. Building on this, the scheme further utilizes another diffusion model to enhance the sample quality, thereby facilitating the data augmentation in scenarios where the original sensing data is insufficient and unevenly distributed. Moreover, we propose a novel algorithm to estimate the acceleration and jerk of signal propagation path length changes from CSI. We then use the proposed scheme to enhance the estimated parameters and detect the number of targets based on the enhanced data. The evaluation reveals that our scheme improves the detection performance by up to 70%, demonstrating reliability and robustness, which supports the deployment and practical use of the ISAC network.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"975-990"},"PeriodicalIF":17.2,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133688","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}
Channel tracking in millimeter wave (mmWave) vehicular systems is crucial for maintaining robust vehicle-to-infrastructure (V2I) communication links, which can be leveraged to achieve high accuracy vehicle position and orientation tracking as a byproduct of communication. While prior work tends to simplify the system model by omitting critical system factors such as clock offsets, filtering effects, antenna array orientation offsets, and channel estimation errors, we address the challenges of a practical mmWave multiple-input multiple-output (MIMO) communication system between a single base station (BS) and a vehicle while tracking the vehicle’s position and orientation (PO) considering realistic driving behaviors. We first develop a channel tracking algorithm based on multidimensional orthogonal matching pursuit (MOMP) with factoring (F-MOMP) to reduce computational complexity and enable high-resolution channel estimates during the tracking stage, suitable for PO estimation. Then, we develop a network called VO-ChAT (Vehicle Orientation-Channel Attention for orientation Tracking), which processes the channel estimate sequence for orientation prediction. Afterward, a weighted least squares (WLS) problem that exploits the channel geometry is formulated to create an initial estimate of the vehicle’s 2D position. A second network named VP-ChAT (Vehicle Position-Channel Attention for position Tracking) refines the geometric position estimate. VP-ChAT is a Transformer inspired network processing the historical channel and position estimates to provide the correction for the initial geometric position estimate. The proposed solution is evaluated using ray-tracing generated channels in an urban canyon environment. In 80% of test cases, the proposed system achieves a 2D position tracking accuracy of 26 cm and maintains orientation errors below 0.5°.
毫米波（mmWave）车载系统中的信道跟踪对于维持稳健的车对基础设施（V2I）通信链路至关重要，这可以作为通信的副产品来实现高精度的车辆位置和方向跟踪。虽然之前的工作倾向于通过忽略关键系统因素（如时钟偏移、滤波效应、天线阵列方向偏移和信道估计误差）来简化系统模型，但我们解决了单个基站（BS）和车辆之间的实际毫米波多输入多输出（MIMO）通信系统的挑战，同时考虑到现实驾驶行为跟踪车辆的位置和方向（PO）。我们首先开发了一种基于多维正交匹配追踪（MOMP）和分解（F-MOMP）的信道跟踪算法，以降低计算复杂度并在跟踪阶段实现高分辨率信道估计，适用于PO估计。然后，我们开发了一个称为VO-ChAT （Vehicle orientation - channel Attention for orientation Tracking）的网络，该网络处理信道估计序列进行方向预测。然后，利用通道几何形状制定加权最小二乘（WLS）问题，以创建车辆二维位置的初始估计。第二个网络称为VP-ChAT (Vehicle position - channel Attention for position Tracking)，对几何位置估计进行了改进。VP-ChAT是一个变压器启发的网络，处理历史信道和位置估计，为初始几何位置估计提供校正。在城市峡谷环境中使用光线跟踪生成的通道对所提出的解决方案进行了评估。在80%的测试用例中，该系统实现了26 cm的2D位置跟踪精度，并将方向误差保持在0.5°以下。
{"title":"A Hybrid Model/Data-Driven Solution to Channel, Position, and Orientation Tracking in mmWave Vehicular Systems","authors":"Yun Chen;Nuria González-Prelcic;Takayuki Shimizu;Chinmay Mahabal","doi":"10.1109/JSAC.2025.3612354","DOIUrl":"10.1109/JSAC.2025.3612354","url":null,"abstract":"Channel tracking in millimeter wave (mmWave) vehicular systems is crucial for maintaining robust vehicle-to-infrastructure (V2I) communication links, which can be leveraged to achieve high accuracy vehicle position and orientation tracking as a byproduct of communication. While prior work tends to simplify the system model by omitting critical system factors such as clock offsets, filtering effects, antenna array orientation offsets, and channel estimation errors, we address the challenges of a practical mmWave multiple-input multiple-output (MIMO) communication system between a single base station (BS) and a vehicle while tracking the vehicle’s position and orientation (PO) considering realistic driving behaviors. We first develop a channel tracking algorithm based on multidimensional orthogonal matching pursuit (MOMP) with factoring (F-MOMP) to reduce computational complexity and enable high-resolution channel estimates during the tracking stage, suitable for PO estimation. Then, we develop a network called VO-ChAT (Vehicle Orientation-Channel Attention for orientation Tracking), which processes the channel estimate sequence for orientation prediction. Afterward, a weighted least squares (WLS) problem that exploits the channel geometry is formulated to create an initial estimate of the vehicle’s 2D position. A second network named VP-ChAT (Vehicle Position-Channel Attention for position Tracking) refines the geometric position estimate. VP-ChAT is a Transformer inspired network processing the historical channel and position estimates to provide the correction for the initial geometric position estimate. The proposed solution is evaluated using ray-tracing generated channels in an urban canyon environment. In 80% of test cases, the proposed system achieves a 2D position tracking accuracy of 26 cm and maintains orientation errors below 0.5°.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"44 ","pages":"927-941"},"PeriodicalIF":17.2,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116988","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 : 2025-09-18DOI: 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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：