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}
The densification of Wi-Fi deployments means that fully distributed random channel access is no longer sufficient for high and predictable performance. Therefore, the upcoming IEEE 802.11bn amendment introduces multi-access point coordination (MAPC) methods. This paper addresses a variant of MAPC called coordinated spatial reuse (C-SR), where devices transmit simultaneously on the same channel, with the power adjusted to minimize interference. The C-SR scheduling problem is selecting which devices transmit concurrently and with what settings. We provide a theoretical upper bound model, optimized for either throughput or fairness, which finds the best possible transmission schedule using mixed-integer linear programming. Then, a practical, probing-based approach is proposed which uses multi-armed bandits (MABs), a type of reinforcement learning, to solve the C-SR scheduling problem. We validate both classical (flat) MAB and hierarchical MAB (H-MAB) schemes with simulations and in a testbed. Using H-MABs for C-SR improves aggregate throughput over legacy IEEE 802.11 (on average by 80% in random scenarios), without reducing the number of transmission opportunities per station. Finally, our framework is lightweight and ready for implementation in Wi-Fi devices.
{"title":"Coordinated Spatial Reuse Scheduling With Machine Learning in IEEE 802.11 MAPC Networks","authors":"Maksymilian Wojnar;Wojciech Ciȩżobka;Artur Tomaszewski;Piotr Chołda;Krzysztof Rusek;Katarzyna Kosek-Szott;Jetmir Haxhibeqiri;Jeroen Hoebeke;Boris Bellalta;Anatolij Zubow;Falko Dressler;Szymon Szott","doi":"10.1109/JSAC.2025.3584555","DOIUrl":"10.1109/JSAC.2025.3584555","url":null,"abstract":"The densification of Wi-Fi deployments means that fully distributed random channel access is no longer sufficient for high and predictable performance. Therefore, the upcoming IEEE 802.11bn amendment introduces multi-access point coordination (MAPC) methods. This paper addresses a variant of MAPC called coordinated spatial reuse (C-SR), where devices transmit simultaneously on the same channel, with the power adjusted to minimize interference. The C-SR scheduling problem is selecting which devices transmit concurrently and with what settings. We provide a theoretical upper bound model, optimized for either throughput or fairness, which finds the best possible transmission schedule using mixed-integer linear programming. Then, a practical, probing-based approach is proposed which uses multi-armed bandits (MABs), a type of reinforcement learning, to solve the C-SR scheduling problem. We validate both classical (flat) MAB and hierarchical MAB (H-MAB) schemes with simulations and in a testbed. Using H-MABs for C-SR improves aggregate throughput over legacy IEEE 802.11 (on average by 80% in random scenarios), without reducing the number of transmission opportunities per station. Finally, our framework is lightweight and ready for implementation in Wi-Fi devices.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3666-3682"},"PeriodicalIF":17.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144547271","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 : 2025-07-02DOI: 10.1109/JSAC.2025.3584562
Zhiyuan Liu;Shuhang Zhang;Qingyu Liu;Hongliang Zhang;Lingyang Song
The radio map presents communication parameters of interest, e.g., received signal strength, at every point across a geographical region. It can be leveraged to improve the efficiency of spectrum utilization in the region, particularly critical for unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize radio samples collected by sensors sparsely distributed in the region to infer a high-resolution radio map. This problem is challenging due to the ultra-low sampling rate, i.e., because the number of available samples is far fewer than the high resolution required for radio map estimation. We propose WiFi-Diffusion – a novel generative framework for achieving fine-grained WiFi radio map estimation using diffusion models. WiFi-Diffusion employs the creative power of generative AI to address the ultra-low sampling rate challenge and consists of three blocks: 1) a boost block, using prior information such as the layout of obstacles to optimize the diffusion model; 2) a generation block, leveraging the diffusion model to generate a candidate set of fine-grained radio maps; and 3) an election block, utilizing the radio propagation model as a guide to find the best fine-grained radio map from the candidate set. Extensive simulations demonstrate that 1) the fine-grained radio map generated by WiFi-Diffusion is ten times better than those produced by state-of-the-art (SOTA) when they use the same ultra-low sampling rate; and 2) WiFi-Diffusion achieves comparable fine-grained radio map quality with only one-fifth of the sampling rate required by SOTA.
{"title":"WiFi-Diffusion: Achieving Fine-Grained WiFi Radio Map Estimation With Ultra-Low Sampling Rate by Diffusion Models","authors":"Zhiyuan Liu;Shuhang Zhang;Qingyu Liu;Hongliang Zhang;Lingyang Song","doi":"10.1109/JSAC.2025.3584562","DOIUrl":"10.1109/JSAC.2025.3584562","url":null,"abstract":"The radio map presents communication parameters of interest, e.g., received signal strength, at every point across a geographical region. It can be leveraged to improve the efficiency of spectrum utilization in the region, particularly critical for unlicensed WiFi spectrum. The problem of fine-grained radio map estimation is to utilize radio samples collected by sensors sparsely distributed in the region to infer a high-resolution radio map. This problem is challenging due to the ultra-low sampling rate, i.e., because the number of available samples is far fewer than the high resolution required for radio map estimation. We propose WiFi-Diffusion – a novel generative framework for achieving fine-grained WiFi radio map estimation using diffusion models. WiFi-Diffusion employs the creative power of generative AI to address the ultra-low sampling rate challenge and consists of three blocks: 1) a boost block, using prior information such as the layout of obstacles to optimize the diffusion model; 2) a generation block, leveraging the diffusion model to generate a candidate set of fine-grained radio maps; and 3) an election block, utilizing the radio propagation model as a guide to find the best fine-grained radio map from the candidate set. Extensive simulations demonstrate that 1) the fine-grained radio map generated by WiFi-Diffusion is ten times better than those produced by state-of-the-art (SOTA) when they use the same ultra-low sampling rate; and 2) WiFi-Diffusion achieves comparable fine-grained radio map quality with only one-fifth of the sampling rate required by SOTA.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3796-3812"},"PeriodicalIF":17.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546929","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 : 2025-07-01DOI: 10.1109/JSAC.2025.3584541
Rubayet Shafin;Iñaki Val;Yue Qi;Peshal Nayak;Vishnu V. Ratnam;Bilal Sadiq;Sigurd Schelstraete;Marcos Martinez;Boon Loong Ng
The increasing demand for high-performance wireless communication, due to emerging applications such as augmented reality, virtual reality, and Internet-of-Things (IoT), has highlighted the need for enhanced Peer-to-Peer (P2P) communication in Wi-Fi networks. P2P communication, often implemented through technologies like Wi-Fi Direct and Wi-Fi Aware, plays a crucial role in enabling seamless device-to-device interaction. This paper explores two significant enhancements for improving P2P communication: enhancing base-channel P2P through the optimization of TXOP sharing for P2P groups, and improving off-channel P2P through multi-AP coordination for channel advertisement. First, we examine the enhancement of base-channel P2P communication by introducing a refined transmission opportunity (TXOP) sharing mechanism, where an AP allocates portions of its TXOP to P2P devices within a group. This allocation ensures that devices can transmit data within a controlled, synchronized framework, thereby reducing contention and improving overall throughput. Furthermore, the proposed improvements enable devices to efficiently share resources based on group-level needs, supporting latency-sensitive applications such as real-time media streaming. Second, we address the challenges of off-channel P2P communication in OBSS (Overlapping Basic Service Set) environments, where interference from neighboring networks can severely affect performance. Through multi-AP coordination, APs can advertise recommended P2P channels that minimize overlap with infrastructure operations, thereby providing cleaner and more reliable channels for P2P communication. In addition, this coordination also facilitates faster setup and more efficient operation of P2P links.
{"title":"Multi-Device Experience With Peer-to-Peer Connectivity in IEEE 802.11bn (Wi-Fi 8)","authors":"Rubayet Shafin;Iñaki Val;Yue Qi;Peshal Nayak;Vishnu V. Ratnam;Bilal Sadiq;Sigurd Schelstraete;Marcos Martinez;Boon Loong Ng","doi":"10.1109/JSAC.2025.3584541","DOIUrl":"10.1109/JSAC.2025.3584541","url":null,"abstract":"The increasing demand for high-performance wireless communication, due to emerging applications such as augmented reality, virtual reality, and Internet-of-Things (IoT), has highlighted the need for enhanced Peer-to-Peer (P2P) communication in Wi-Fi networks. P2P communication, often implemented through technologies like Wi-Fi Direct and Wi-Fi Aware, plays a crucial role in enabling seamless device-to-device interaction. This paper explores two significant enhancements for improving P2P communication: enhancing base-channel P2P through the optimization of TXOP sharing for P2P groups, and improving off-channel P2P through multi-AP coordination for channel advertisement. First, we examine the enhancement of base-channel P2P communication by introducing a refined transmission opportunity (TXOP) sharing mechanism, where an AP allocates portions of its TXOP to P2P devices within a group. This allocation ensures that devices can transmit data within a controlled, synchronized framework, thereby reducing contention and improving overall throughput. Furthermore, the proposed improvements enable devices to efficiently share resources based on group-level needs, supporting latency-sensitive applications such as real-time media streaming. Second, we address the challenges of off-channel P2P communication in OBSS (Overlapping Basic Service Set) environments, where interference from neighboring networks can severely affect performance. Through multi-AP coordination, APs can advertise recommended P2P channels that minimize overlap with infrastructure operations, thereby providing cleaner and more reliable channels for P2P communication. In addition, this coordination also facilitates faster setup and more efficient operation of P2P links.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3695-3707"},"PeriodicalIF":17.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144533166","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 : 2025-06-30DOI: 10.1109/JSAC.2025.3584507
Prasad Netalkar;Carlos E. Caicedo Bastidas;Igor Kadota;Gil Zussman;Ivan Seskar;Dipankar Raychaudhuri
Dynamic Spectrum Access (DSA) is a key mechanism for meeting the ever-increasing demand for emerging wireless services. DSA involves managing and assigning available spectrum resources in a way that minimizes interference and allows RF coexistence between heterogeneous devices and systems. Such co-existence mechanisms, if they are to succeed when heterogeneous RF devices managed by different entities need to operate in a given area and frequency band (licensed and/or unlicensed), require a common mechanism for expressing the boundaries of spectrum use of each device so that spectrum use deconfliction methods can be built and verified. Spectrum Consumption Models (SCMs) – defined in the IEEE 1900.5.2 standard – offer a mechanism for RF devices to: (i) declare the characteristics of their intended spectrum use and their interference protection needs; and (ii) determine compatibility (non-interference) with existing devices. In this paper, we propose a novel SCM-based Spectrum Deconfliction (SD) algorithm that dynamically configures RF operational parameters (e.g., center frequency and transmission power) of a target transmitter-receiver pair aiming to minimize interference with existing devices/systems. We also propose sequential and distributed DSA methods that use the SD algorithm for assigning spectrum in large-scale networks. To evaluate the performance of our methods in terms of computation time, spectrum assignment efficiency, and overhead, we use two custom-made simulation platforms. Finally, to experimentally demonstrate the feasibility of our methods, we build a proof-of-concept implementation in the NSF PAWR COSMOS wireless testbed. The results reveal the advantages of using SCMs and their capabilities to conduct spectrum assignments in dynamic and congested communication environments.
{"title":"Scalable Dynamic Spectrum Access With IEEE 1900.5.2 Spectrum Consumption Models","authors":"Prasad Netalkar;Carlos E. Caicedo Bastidas;Igor Kadota;Gil Zussman;Ivan Seskar;Dipankar Raychaudhuri","doi":"10.1109/JSAC.2025.3584507","DOIUrl":"10.1109/JSAC.2025.3584507","url":null,"abstract":"Dynamic Spectrum Access (DSA) is a key mechanism for meeting the ever-increasing demand for emerging wireless services. DSA involves managing and assigning available spectrum resources in a way that minimizes interference and allows RF coexistence between heterogeneous devices and systems. Such co-existence mechanisms, if they are to succeed when heterogeneous RF devices managed by different entities need to operate in a given area and frequency band (licensed and/or unlicensed), require a common mechanism for expressing the boundaries of spectrum use of each device so that spectrum use deconfliction methods can be built and verified. Spectrum Consumption Models (SCMs) – defined in the IEEE 1900.5.2 standard – offer a mechanism for RF devices to: (i) declare the characteristics of their intended spectrum use and their interference protection needs; and (ii) determine compatibility (non-interference) with existing devices. In this paper, we propose a novel SCM-based Spectrum Deconfliction (SD) algorithm that dynamically configures RF operational parameters (e.g., center frequency and transmission power) of a target transmitter-receiver pair aiming to minimize interference with existing devices/systems. We also propose sequential and distributed DSA methods that use the SD algorithm for assigning spectrum in large-scale networks. To evaluate the performance of our methods in terms of computation time, spectrum assignment efficiency, and overhead, we use two custom-made simulation platforms. Finally, to experimentally demonstrate the feasibility of our methods, we build a proof-of-concept implementation in the NSF PAWR COSMOS wireless testbed. The results reveal the advantages of using SCMs and their capabilities to conduct spectrum assignments in dynamic and congested communication environments.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3830-3845"},"PeriodicalIF":17.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520623","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 : 2025-06-30DOI: 10.1109/JSAC.2025.3584496
Shumin Lian;Jingwen Tong;Jun Zhang;Liqun Fu
WiFi networks have achieved remarkable success in enabling seamless communication and data exchange worldwide. The IEEE 802.11be standard, known as WiFi 7, introduces Multi-Link Operation (MLO), a groundbreaking feature that enables devices to establish multiple simultaneous connections across different bands and channels. While MLO promises substantial improvements in network throughput and latency reduction, it presents significant challenges in channel allocation, particularly in dense network environments. Current research has predominantly focused on performance analysis and throughput optimization within static WiFi 7 network configurations. In contrast, this paper addresses the dynamic channel allocation problem in dense WiFi 7 networks with MLO capabilities. We formulate this challenge as a combinatorial optimization problem, leveraging a novel network performance analysis mechanism. Given the inherent lack of prior network information, we model the problem within a Multi-Armed Bandit (MAB) framework to enable online learning of optimal channel allocations. Our proposed Best-Arm Identification-enabled Monte Carlo Tree Search (BAI-MCTS) algorithm includes rigorous theoretical analysis, providing upper bounds for both sample complexity and error probability. To further reduce sample complexity and enhance generalizability across diverse network scenarios, we put forth LLM-BAI-MCTS, an intelligent algorithm for the dynamic channel allocation problem by integrating the Large Language Model (LLM) into the BAI-MCTS algorithm. Numerical results demonstrate that the BAI-MCTS algorithm achieves a convergence rate approximately 50.44% faster than the state-of-the-art algorithms when reaching 98% of the optimal value. Notably, the convergence rate of the LLM-BAI-MCTS algorithm increases by over 63.32% in dense networks.
{"title":"Intelligent Channel Allocation for IEEE 802.11be Multi-Link Operation: When MAB Meets LLM","authors":"Shumin Lian;Jingwen Tong;Jun Zhang;Liqun Fu","doi":"10.1109/JSAC.2025.3584496","DOIUrl":"10.1109/JSAC.2025.3584496","url":null,"abstract":"WiFi networks have achieved remarkable success in enabling seamless communication and data exchange worldwide. The IEEE 802.11be standard, known as WiFi 7, introduces Multi-Link Operation (MLO), a groundbreaking feature that enables devices to establish multiple simultaneous connections across different bands and channels. While MLO promises substantial improvements in network throughput and latency reduction, it presents significant challenges in channel allocation, particularly in dense network environments. Current research has predominantly focused on performance analysis and throughput optimization within static WiFi 7 network configurations. In contrast, this paper addresses the dynamic channel allocation problem in dense WiFi 7 networks with MLO capabilities. We formulate this challenge as a combinatorial optimization problem, leveraging a novel network performance analysis mechanism. Given the inherent lack of prior network information, we model the problem within a Multi-Armed Bandit (MAB) framework to enable online learning of optimal channel allocations. Our proposed Best-Arm Identification-enabled Monte Carlo Tree Search (BAI-MCTS) algorithm includes rigorous theoretical analysis, providing upper bounds for both sample complexity and error probability. To further reduce sample complexity and enhance generalizability across diverse network scenarios, we put forth LLM-BAI-MCTS, an intelligent algorithm for the dynamic channel allocation problem by integrating the Large Language Model (LLM) into the BAI-MCTS algorithm. Numerical results demonstrate that the BAI-MCTS algorithm achieves a convergence rate approximately 50.44% faster than the state-of-the-art algorithms when reaching 98% of the optimal value. Notably, the convergence rate of the LLM-BAI-MCTS algorithm increases by over 63.32% in dense networks.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3650-3665"},"PeriodicalIF":17.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520563","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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