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}
Pub Date : 2025-06-30DOI: 10.1109/JSAC.2025.3584564
Stefano Avallone;Pasquale Imputato
The IEEE 802.11 working group is currently finalizing the 802.11be amendment, which defines the features that will be supported by Wi-Fi 7 devices. A prominent new feature, termed Multi-Link Operations (MLO), is the ability for a device to operate on multiple links, i.e., on multiple frequency channels. Among the various MLO modes defined, Enhanced Multi-Link Single Radio (EMLSR) is attracting the interest of many vendors due to its potential for exploiting operations on multiple links through reduced hardware capabilities. In this paper, we first provide an overview of the standard specifications for EMLSR and describe the model underlying its implementation that we have contributed to the ns-3 simulator. The implemented model is rather flexible and enables to simulate various architectures differing for implementation cost, power consumption and performance. Then, we thoroughly evaluate several EMLSR configurations with the goal of shedding light on the possible alternatives that are available. We consider both a scenario of saturated conditions without interfering traffic and a scenario of non-saturated conditions with interfering traffic. Our study shows that the main differences in performance among the various EMLSR configurations are observed in the uplink direction and that EMLSR operations enable to reduce latency with respect to single-link devices at the cost of a slight increase in power consumption.
{"title":"Understanding the New Enhanced Multi-Link Single Radio Feature of IEEE 802.11be WLANs","authors":"Stefano Avallone;Pasquale Imputato","doi":"10.1109/JSAC.2025.3584564","DOIUrl":"10.1109/JSAC.2025.3584564","url":null,"abstract":"The IEEE 802.11 working group is currently finalizing the 802.11be amendment, which defines the features that will be supported by Wi-Fi 7 devices. A prominent new feature, termed Multi-Link Operations (MLO), is the ability for a device to operate on multiple <italic>links</i>, i.e., on multiple frequency channels. Among the various MLO modes defined, Enhanced Multi-Link Single Radio (EMLSR) is attracting the interest of many vendors due to its potential for exploiting operations on multiple links through reduced hardware capabilities. In this paper, we first provide an overview of the standard specifications for EMLSR and describe the model underlying its implementation that we have contributed to the ns-3 simulator. The implemented model is rather flexible and enables to simulate various architectures differing for implementation cost, power consumption and performance. Then, we thoroughly evaluate several EMLSR configurations with the goal of shedding light on the possible alternatives that are available. We consider both a scenario of saturated conditions without interfering traffic and a scenario of non-saturated conditions with interfering traffic. Our study shows that the main differences in performance among the various EMLSR configurations are observed in the uplink direction and that EMLSR operations enable to reduce latency with respect to single-link devices at the cost of a slight increase in power consumption.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3683-3694"},"PeriodicalIF":17.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520572","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.2025.3584515
Juan Fang;Qinghua Li;Cheng Chen;Assaf Gurevitz;Yaron Yoffe
The IEEE 802.11 working group has formed a new Task Group, 802.11bn, to develop a new amendment to support ultra-high reliability (UHR) for Wi-Fi networks, which will eventually shape what Wi-Fi 8 will look like. In this paper, we propose a probabilistic shaping (PS) scheme to improve the spectrum and power efficiency in medium to high signal to noise ratio (SNR) regime for Wi-Fi 8. The integration and compatibility with legacy Wi-Fi systems, as well as other Wi-Fi 8 candidate technologies like unequal modulation (UEQM) are addressed. An architecture with a single low-density parity-check (LDPC) encoder and multiple shaping encoders is devised to adapt to different qualities of spatial channels. Furthermore, we propose practical techniques to resolve issues like error propagation, scrambler re-synchronization, and packet length determination to ensure compatibility with legacy scrambling, subframe detection, and packaging flow. It is shown that the proposed constellation shaping scheme provides average 0.89 dB shaping gains over the legacy Wi-Fi scheme, and the shaping gains remain when UEQM and lifted LDPC are applied.
{"title":"Probabilistic Shaping for Wi-Fi 8","authors":"Juan Fang;Qinghua Li;Cheng Chen;Assaf Gurevitz;Yaron Yoffe","doi":"10.1109/JSAC.2025.3584515","DOIUrl":"10.1109/JSAC.2025.3584515","url":null,"abstract":"The IEEE 802.11 working group has formed a new Task Group, 802.11bn, to develop a new amendment to support ultra-high reliability (UHR) for Wi-Fi networks, which will eventually shape what Wi-Fi 8 will look like. In this paper, we propose a probabilistic shaping (PS) scheme to improve the spectrum and power efficiency in medium to high signal to noise ratio (SNR) regime for Wi-Fi 8. The integration and compatibility with legacy Wi-Fi systems, as well as other Wi-Fi 8 candidate technologies like unequal modulation (UEQM) are addressed. An architecture with a single low-density parity-check (LDPC) encoder and multiple shaping encoders is devised to adapt to different qualities of spatial channels. Furthermore, we propose practical techniques to resolve issues like error propagation, scrambler re-synchronization, and packet length determination to ensure compatibility with legacy scrambling, subframe detection, and packaging flow. It is shown that the proposed constellation shaping scheme provides average 0.89 dB shaping gains over the legacy Wi-Fi scheme, and the shaping gains remain when UEQM and lifted LDPC are applied.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3708-3721"},"PeriodicalIF":17.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520564","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.2025.3584427
Youngwook Son;Saewoong Bahk
There have been long efforts to refine Wi-Fi carrier sensing (CS) for more aggressive channel access, in pursuit of enhanced network performance. To this end, the recent 802.11ax amendment introduced a preamble detection (PD)-based spatial reuse, allowing concurrent transmissions between adjacent links via adjustable sensitivity levels. Against these conventional ideas, this paper presents a different perspective: Wi-Fi devices already have excessive transmission (TX) opportunities in practice, even without detecting each other under certain scenarios. We shed light on CS anomalies relevant to undetected preambles, which not only cause adjacent devices to transmit concurrently but are also triggered by the new PD-based mechanism, ultimately disrupting its intended operations. Our testbed experiments and in-depth scrutiny reveal the dominant impact of these anomalies on overall network behaviors. Based on these insights, we present two comprehensive frameworks, $textsf {REFRAIN}$ and $textsf {AdOPT}$ , to fully exploit TX opportunities enabled by the anomalies and PD-based mechanism respectively, for practical spatial reuse. Prototypes using commercial Wi-Fi devices and NI USRP show the feasibility and effectiveness of our approaches. Extensive simulation results further demonstrate that $textsf {REFRAIN}$ and $textsf {AdOPT}$ achieve up to 1.94$times$ and 1.61$times$ higher average throughput, only with reduced transmission attempts by half, highlighting their potential to elevate network capacity and efficiency in practical Wi-Fi networks.
{"title":"Bringing Spatial Reuse Into Practice for Distributed Wi-Fi Networks: Preamble Detection and Anomalies","authors":"Youngwook Son;Saewoong Bahk","doi":"10.1109/JSAC.2025.3584427","DOIUrl":"10.1109/JSAC.2025.3584427","url":null,"abstract":"There have been long efforts to refine Wi-Fi carrier sensing (CS) for more aggressive channel access, in pursuit of enhanced network performance. To this end, the recent 802.11ax amendment introduced a preamble detection (PD)-based spatial reuse, allowing concurrent transmissions between adjacent links via adjustable sensitivity levels. Against these conventional ideas, this paper presents a different perspective: Wi-Fi devices already have excessive transmission (TX) opportunities in practice, even without detecting each other under certain scenarios. We shed light on CS anomalies relevant to undetected preambles, which not only cause adjacent devices to transmit concurrently but are also triggered by the new PD-based mechanism, ultimately disrupting its intended operations. Our testbed experiments and in-depth scrutiny reveal the dominant impact of these anomalies on overall network behaviors. Based on these insights, we present two comprehensive frameworks, <inline-formula> <tex-math>$textsf {REFRAIN}$ </tex-math></inline-formula> and <inline-formula> <tex-math>$textsf {AdOPT}$ </tex-math></inline-formula>, to fully exploit TX opportunities enabled by the anomalies and PD-based mechanism respectively, for practical spatial reuse. Prototypes using commercial Wi-Fi devices and NI USRP show the feasibility and effectiveness of our approaches. Extensive simulation results further demonstrate that <inline-formula> <tex-math>$textsf {REFRAIN}$ </tex-math></inline-formula> and <inline-formula> <tex-math>$textsf {AdOPT}$ </tex-math></inline-formula> achieve up to 1.94<inline-formula> <tex-math>$times$ </tex-math></inline-formula> and 1.61<inline-formula> <tex-math>$times$ </tex-math></inline-formula> higher average throughput, only with reduced transmission attempts by half, highlighting their potential to elevate network capacity and efficiency in practical Wi-Fi networks.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3616-3632"},"PeriodicalIF":17.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520568","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.2025.3584497
Yayu Gao;Muyuan Shen;Yu Zou;Hao Yin;Sumit Roy
As a groundbreaking feature in IEEE 802.11be, multi-link operation (MLO) is expected to support emerging applications that are strongly delay-sensitive. A key to the effective use of MLO for such cases rests on the optimal allocation of application traffic across multiple links. Our initial simulation experiments in ns-3 reveal that the proposed traffic allocation policies in prior art are significantly sub-optimal in terms of achievable delay performance of multi-link devices (MLDs), particularly in the presence of legacy single-link devices (SLDs). In this work, we first develop a new analytical model for the mean end-to-end (E2E) delay, delay jitter and worst-case percentile latency performance of MLD-SLD-coexisting Wi-Fi 7 networks (largely unexplored to date) with saturated and unsaturated SLD traffic. Subsequently, the optimal traffic allocation strategies for minimizing the mean E2E delay and delay jitter are obtained and validated by ns-3 simulation results. It is shown that with the optimal policy, MLDs can achieve significantly better mean E2E delay, delay jitter, worst-case latency and delay cumulative distribution function (CDF) compared to existing solutions.
{"title":"Latency Optimal Traffic-to-Link Allocation for MLO/SLO Coexistence in Wi-Fi 7","authors":"Yayu Gao;Muyuan Shen;Yu Zou;Hao Yin;Sumit Roy","doi":"10.1109/JSAC.2025.3584497","DOIUrl":"10.1109/JSAC.2025.3584497","url":null,"abstract":"As a groundbreaking feature in IEEE 802.11be, multi-link operation (MLO) is expected to support emerging applications that are strongly delay-sensitive. A key to the effective use of MLO for such cases rests on the optimal allocation of application traffic across multiple links. Our initial simulation experiments in ns-3 reveal that the proposed traffic allocation policies in prior art are significantly sub-optimal in terms of achievable delay performance of multi-link devices (MLDs), particularly in the presence of legacy single-link devices (SLDs). In this work, we first develop a new analytical model for the mean end-to-end (E2E) delay, delay jitter and worst-case percentile latency performance of MLD-SLD-coexisting Wi-Fi 7 networks (largely unexplored to date) with saturated and unsaturated SLD traffic. Subsequently, the optimal traffic allocation strategies for minimizing the mean E2E delay and delay jitter are obtained and validated by ns-3 simulation results. It is shown that with the optimal policy, MLDs can achieve significantly better mean E2E delay, delay jitter, worst-case latency and delay cumulative distribution function (CDF) compared to existing solutions.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3633-3649"},"PeriodicalIF":17.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520622","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.2025.3584510
Xin Li;Jingzhi Hu;Hongbo Wang;Zhe Chen;Jun Luo
As a technology with ubiquitous presence in unlicensed spectrum, Wi-Fi has demonstrated prominent capabilities in both communication and sensing. However, since the bandwidth requirements for communication and sensing differ significantly, channel bandwidths excessive for communication (e.g., 160 MHz) still fail to achieve multi-person sensing. Though stitching multiple consecutive channels to expand the effective bandwidth sounds plausible, it may never reach ultra-wideband (UWB) in practice. To this end, we propose UWB-Fi as a novel Wi-Fi sensing framework with ultra-wide bandwidth, leveraging only discrete and irregular channel samples. We first design a fast channel hopping scheme to enable arbitrary channel sampling across 4.7 GHz bandwidth on commodity Wi-Fi hardware without interrupting default communications. As no algorithm exists to exploit such channel samples, we establish a theoretical analysis driven by compressive sensing, so as to enable an explainable deep learning model. This model transforms sparse channel samples into high-dimensional (position) spectra, effectively avoiding the bias-variance dilemma in parameter estimation while encoding sufficient information for general sensing. Our extensive evaluations demonstrate that UWB-Fi successfully achieves centimeter-level fine-granularity multi-person sensing.
{"title":"Enabling Ultra-Wideband Wi-Fi Sensing via Sparse Channel Sampling","authors":"Xin Li;Jingzhi Hu;Hongbo Wang;Zhe Chen;Jun Luo","doi":"10.1109/JSAC.2025.3584510","DOIUrl":"10.1109/JSAC.2025.3584510","url":null,"abstract":"As a technology with ubiquitous presence in unlicensed spectrum, Wi-Fi has demonstrated prominent capabilities in both communication and sensing. However, since the bandwidth requirements for communication and sensing differ significantly, channel bandwidths excessive for communication (e.g., 160 MHz) still fail to achieve multi-person sensing. Though stitching multiple consecutive channels to expand the effective bandwidth sounds plausible, it may never reach <italic>ultra-wideband</i> (UWB) in practice. To this end, we propose UWB-Fi as a novel Wi-Fi sensing framework with ultra-wide bandwidth, leveraging only discrete and irregular channel samples. We first design a fast channel hopping scheme to enable arbitrary channel sampling across 4.7 GHz bandwidth on commodity Wi-Fi hardware without interrupting default communications. As no algorithm exists to exploit such channel samples, we establish a theoretical analysis driven by <italic>compressive sensing</i>, so as to enable an <italic>explainable</i> deep learning model. This model transforms sparse channel samples into high-dimensional (position) spectra, effectively avoiding the <italic>bias-variance dilemma</i> in parameter estimation while encoding sufficient information for general sensing. Our extensive evaluations demonstrate that UWB-Fi successfully achieves centimeter-level fine-granularity multi-person sensing.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":"43 11","pages":"3782-3795"},"PeriodicalIF":17.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144520565","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}
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：