PHY-Aware MCS Adaptation for NR V2X Mode 2 with ML-Driven Resource Allocation

PHY-Aware MCS Adaptation for NR V2X Mode 2 with ML-Driven Resource Allocation | 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 PHY-Aware MCS Adaptation for NR V2X Mode 2 with ML-Driven Resource Allocation ANNU ANNU, Prof. Rajalakshmi Pachamuthu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7192344/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This paper proposes a unified framework to enhance NR V2X sidelink Mode 2 by combining PHY-aware MCS adaptation with reinforcement learning (RL)-based resource allocation. In out-of-coverage scenarios, vehicles select MCS based on real-time SINR using thresholds from the UWICORE dataset, while an RL agent optimizes semi-persistent scheduling to avoid collisions. Analytical models for packet delivery, collision probability, and spectral efficiency are developed and validated using WiLabV2X simulations. Results demonstrate improved PDR, reduced collisions, and higher energy and spectral efficiency over standard SPS, particularly in dense traffic, aligning with 3GPP TR 37.885 for safety-critical use. NR V2X sidelink Mode 2 resource allocation machine learning reinforcement learning MCS adaptation analytical framework Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialPhyMCS.zip SupplementaryMaterialPhyMCS.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Oct, 2025 Reviews received at journal 25 Sep, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 12 Sep, 2025 Editor assigned by journal 24 Jul, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 23 Jul, 2025 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. 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