Federated Reinforcement Learning for Distributed MAC Optimization in IEEE 802.11bn Networks

Federated Reinforcement Learning for Distributed MAC Optimization in IEEE 802.11bn Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Federated Reinforcement Learning for Distributed MAC Optimization in IEEE 802.11bn Networks Vijay B T This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7128890/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract IEEE 802.11bn (Wi-Fi 8) introduces Multi-AP Coordination (MAPC) to meet ultra-reliable low-latency communication (URLLC) demands in dense wireless deployments. While centralized MAC-layer scheduling improves coordination, it introduces overhead, privacy risks, and scalability challenges. In this paper, propose a decentralized federated reinforcement learning (FRL) framework for MAC scheduling across distributed access points (APs). Each AP independently learns optimal transmission policies using deep Q-learning, while periodically synchronizing model updates through a lightweight federated server. This approach preserves local traffic privacy and reduces control latency, without sacrificing performance or adaptability. The system dynamically adjusts to varying interference, heterogeneous traffic loads, and dynamic topology changes in real-time. Evaluate the proposed FRL-based scheduler under diverse STA densities, mobility scenarios, and stochastic channel conditions using extensive custom simulations. Results show that our model achieves up to 29% lower latency, 22% higher fairness, and 17% reduction in signaling overhead compared to centralized RL and OFDMA-based MAC methods. The proposed solution offers a scalable, privacy-preserving, and resilient path toward intelligent MAC optimization in next-generation Wi-Fi networks, paving the way for mission-critical industrial and latency-sensitive applications. Federated Reinforcement Learning (FRL) Multi-AP Coordination (MAPC) Wi-Fi 8 (IEEE 802.11bn) Medium Access Control (MAC) Latency Optimization Distributed Scheduling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Federated Reinforcement Learning (FRL), Multi-AP Coordination (MAPC), Wi-Fi 8 (IEEE 802.11bn), Medium Access Control (MAC), Latency Optimization, Distributed Scheduling","lastPublishedDoi":"10.21203/rs.3.rs-7128890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7128890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIEEE 802.11bn (Wi-Fi 8) introduces Multi-AP Coordination (MAPC) to meet ultra-reliable low-latency communication (URLLC) demands in dense wireless deployments. While centralized MAC-layer scheduling improves coordination, it introduces overhead, privacy risks, and scalability challenges. In this paper, propose a decentralized federated reinforcement learning (FRL) framework for MAC scheduling across distributed access points (APs). Each AP independently learns optimal transmission policies using deep Q-learning, while periodically synchronizing model updates through a lightweight federated server. This approach preserves local traffic privacy and reduces control latency, without sacrificing performance or adaptability. The system dynamically adjusts to varying interference, heterogeneous traffic loads, and dynamic topology changes in real-time. Evaluate the proposed FRL-based scheduler under diverse STA densities, mobility scenarios, and stochastic channel conditions using extensive custom simulations. 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