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}
Pub Date : 2025-07-14DOI: 10.1109/JSAC.2025.3584549
Fangming Zhao;Nikolaos Pappas;Meng Zhang;Howard H. Yang
We study the age of information (AoI) in a random access network consisting of multiple source-destination pairs, where each source node is empowered by energy harvesting capability. Every source node transmits a sequence of data packets to its destination using only the harvested energy. Each data packet is encoded with finite-length codewords, characterizing the nature of short codeword transmissions in random access networks. By combining tools from bulk-service Markov chains with stochastic geometry, we derive an analytical expression for the network average AoI and obtain closed-form results in two special cases, i.e., the small and large energy buffer size scenarios. Our analysis reveals the trade-off between energy accumulation time and transmission success probability. We then optimize the network average AoI by jointly adjusting the update rate and the blocklength of the data packet. Our findings indicate that the optimal update rate should be set to one in the energy-constrained regime where the energy consumption rate exceeds the energy arrival rate. This also means if the optimal blocklength of the data packet is pre-configured, an energy buffer size supporting only one transmission is sufficient.
{"title":"Age of Information in Random Access Networks With Energy Harvesting","authors":"Fangming Zhao;Nikolaos Pappas;Meng Zhang;Howard H. Yang","doi":"10.1109/JSAC.2025.3584549","DOIUrl":"10.1109/JSAC.2025.3584549","url":null,"abstract":"We study the age of information (AoI) in a random access network consisting of multiple source-destination pairs, where each source node is empowered by energy harvesting capability. Every source node transmits a sequence of data packets to its destination using only the harvested energy. Each data packet is encoded with finite-length codewords, characterizing the nature of short codeword transmissions in random access networks. By combining tools from bulk-service Markov chains with stochastic geometry, we derive an analytical expression for the network average AoI and obtain closed-form results in two special cases, i.e., the small and large energy buffer size scenarios. Our analysis reveals the trade-off between energy accumulation time and transmission success probability. We then optimize the network average AoI by jointly adjusting the update rate and the blocklength of the data packet. Our findings indicate that the optimal update rate should be set to one in the energy-constrained regime where the energy consumption rate exceeds the energy arrival rate. This also means if the optimal blocklength of the data packet is pre-configured, an energy buffer size supporting only one transmission is sufficient.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3813-3829"},"PeriodicalIF":17.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629573","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}
Accurate 3D localization is essential for realizing advanced sensing functionalities in next-generation Wi-Fi communication systems. This study investigates the potential of multistatic localization in Wi-Fi networks through the deployment of multiple cooperative antenna arrays. The collaborative gain offered by these arrays is twofold: 1) intra-array coherent gain at the wavelength scale among antenna elements, and 2) inter-array cooperative gain across arrays. To evaluate the feasibility and performance of this approach, we develop WiCAL (Wi-Fi Collaborative Antenna Localization), a system built upon commercial Wi-Fi infrastructure equipped with uniform rectangular arrays (URAs). These arrays are driven by multiplexing embedded radio frequency (RF) chains available in standard access points or user devices, thereby eliminating the need for sophisticated, costly, and power-hungry multi-transceiver modules typically required in multiple-input and multiple-output (MIMO) systems. To address phase offsets introduced by RF chain multiplexing, we propose a three-stage, fine-grained phase alignment scheme to synchronize signals across antenna elements within each array. A bidirectional spatial smoothing MUSIC algorithm is employed to estimate angles of arrival (AoAs) and mitigate performance degradation caused by correlated interference. To further exploit inter-array cooperative gain, we elaborate on the synchronization mechanism among distributed URAs, which enables direct position determination by bypassing intermediate angle estimation. Once synchronized, the distributed URAs effectively form a virtual large-scale array, significantly enhancing spatial resolution and localization accuracy. WiCAL is validated using $3 times 4$ URAs operating at the 5.2 GHz band. Experimental results demonstrate median AoA estimation errors of 1° in elevation and 1.5° in azimuth under intra-array coherent processing. For inter-array collaboration, the system achieves a median localization error of 15.6 cm using two URAs, outperforming state-of-the-art methods.
{"title":"WiCAL: Accurate Wi-Fi-Based 3D Localization Enabled by Collaborative Antenna Arrays","authors":"Fuhai Wang;Zhe Li;Rujing Xiong;Tiebin Mi;Robert Caiming Qiu","doi":"10.1109/JSAC.2025.3584540","DOIUrl":"10.1109/JSAC.2025.3584540","url":null,"abstract":"Accurate 3D localization is essential for realizing advanced sensing functionalities in next-generation Wi-Fi communication systems. This study investigates the potential of multistatic localization in Wi-Fi networks through the deployment of multiple cooperative antenna arrays. The collaborative gain offered by these arrays is twofold: 1) intra-array coherent gain at the wavelength scale among antenna elements, and 2) inter-array cooperative gain across arrays. To evaluate the feasibility and performance of this approach, we develop WiCAL (Wi-Fi Collaborative Antenna Localization), a system built upon commercial Wi-Fi infrastructure equipped with uniform rectangular arrays (URAs). These arrays are driven by multiplexing embedded radio frequency (RF) chains available in standard access points or user devices, thereby eliminating the need for sophisticated, costly, and power-hungry multi-transceiver modules typically required in multiple-input and multiple-output (MIMO) systems. To address phase offsets introduced by RF chain multiplexing, we propose a three-stage, fine-grained phase alignment scheme to synchronize signals across antenna elements within each array. A bidirectional spatial smoothing MUSIC algorithm is employed to estimate angles of arrival (AoAs) and mitigate performance degradation caused by correlated interference. To further exploit inter-array cooperative gain, we elaborate on the synchronization mechanism among distributed URAs, which enables direct position determination by bypassing intermediate angle estimation. Once synchronized, the distributed URAs effectively form a virtual large-scale array, significantly enhancing spatial resolution and localization accuracy. WiCAL is validated using <inline-formula> <tex-math>$3 times 4$ </tex-math></inline-formula> URAs operating at the 5.2 GHz band. Experimental results demonstrate median AoA estimation errors of 1° in elevation and 1.5° in azimuth under intra-array coherent processing. For inter-array collaboration, the system achieves a median localization error of 15.6 cm using two URAs, outperforming state-of-the-art methods.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3752-3765"},"PeriodicalIF":17.2,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577978","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 : 2025-07-03DOI: 10.1109/JSAC.2025.3584433
Jianwei Liu;Jiantao Yuan;Guanding Yu;Jinsong Han
WiFi-based gesture recognition (WGR) has emerged as a promising technology due to its potential for integration with communication systems under the concept of integrated sensing and communication (ISAC). However, current WGR systems face two primary challenges: limited scalability for recognizing new gestures and poor compatibility with ISAC. These systems typically require extensive data collection and retraining for each new gesture and struggle to handle the dimensional variability of channel state information (CSI) caused by fluctuating data traffic in communication networks. To overcome these limitations, we introduce OneSense, a one-shot WGR system designed for seamless integration with communication systems. OneSense designs a data enrichment technique based on the law of signal propagation to generate virtual gestures. Based on enriched dataset, OneSense leverages an aug-meta learning (AML) framework to facilitate efficient and scalable FSL. OneSense also incorporates a data cropping strategy to enhance gesture feature prominence and a dynamic size-adaptive backbone model that ensures compatibility with CSI samples exhibiting dimensional inconsistencies. Experimental results show that OneSense achieves over 94% accuracy in one-shot gesture recognition. A case study further illustrates its effectiveness in ISAC contexts. Furthermore, our proposed AML framework reduces pre-training latency by more than 86% compared to conventional meta-learning approaches.
{"title":"Efficient One-Shot Gesture Recognition for WiFi ISAC via Aug-Meta Learning","authors":"Jianwei Liu;Jiantao Yuan;Guanding Yu;Jinsong Han","doi":"10.1109/JSAC.2025.3584433","DOIUrl":"10.1109/JSAC.2025.3584433","url":null,"abstract":"WiFi-based gesture recognition (WGR) has emerged as a promising technology due to its potential for integration with communication systems under the concept of integrated sensing and communication (ISAC). However, current WGR systems face two primary challenges: limited scalability for recognizing new gestures and poor compatibility with ISAC. These systems typically require extensive data collection and retraining for each new gesture and struggle to handle the dimensional variability of channel state information (CSI) caused by fluctuating data traffic in communication networks. To overcome these limitations, we introduce OneSense, a one-shot WGR system designed for seamless integration with communication systems. OneSense designs a data enrichment technique based on the law of signal propagation to generate virtual gestures. Based on enriched dataset, OneSense leverages an aug-meta learning (AML) framework to facilitate efficient and scalable FSL. OneSense also incorporates a data cropping strategy to enhance gesture feature prominence and a dynamic size-adaptive backbone model that ensures compatibility with CSI samples exhibiting dimensional inconsistencies. Experimental results show that OneSense achieves over 94% accuracy in one-shot gesture recognition. A case study further illustrates its effectiveness in ISAC contexts. Furthermore, our proposed AML framework reduces pre-training latency by more than 86% compared to conventional meta-learning approaches.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3766-3781"},"PeriodicalIF":17.2,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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