Resource Allocation for Out‑of‑Coverage V2V Using an Actor–Critic CTDE | 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 Resource Allocation for Out‑of‑Coverage V2V Using an Actor–Critic CTDE youssef oummany, Fouzia Boukour Elbahhar, Raja El Assali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7516747/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Apr, 2026 Read the published version in Journal on Wireless Communications and Networking → Version 1 posted You are reading this latest preprint version Abstract Vehicular communications are critical for road safety, but out-of-coverage Vehicle-to-Vehicle (V2V) links face stringent latency and reliability demands in highly mobile, spectrum-congested environments. 5G New Radio-Vehicle-to-Everything (NR-V2X) Mode 2 lets vehicles semi-persistently reserve control (Physical Sidelink Control Channel) and data (Physical Sidelink Data Channel) resources, yet collisions and imperfect sensing of primary users (PUs) still lead to significant performance degradation. We propose a multi-agent resource allocation framework based on Centralized Training with Decentralized Execution (CTDE) and an Actor–Critic architecture. Each V2V link runs a lightweight Long Short-Term Memory (LSTM)-based Actor that outputs a continuous data subcarrier “budget” via a log-normal policy, while a reactive semi-persistent Physical Sidelink Control Channel/Physical Sidelink Data Channel scheduler handles control-block collisions and PU detection. During training, a global LSTM Critic observes all channel qualities and sensing masks, stabilizing value estimates despite aging Channel State Information (CSI) and sensing errors. We define a reward that balances Packet Reception Ratio (PRR) and a satisfaction index across three service levels (SSV+, C1, C2), fostering cooperation under a 100 ms latency constraint. Our simulations—integrating Simulation of Urban MObility (SUMO) mobility, 3rd Generation Partnership Project (3GPP) Urban Microcell (UMi) Line-of-Sight/Non-Line-of-Sight (LOS/NLOS) path loss with log-normal shadowing, multi-tap Tapped Delay Line (TDL) Doppler fading, Demodulation Reference Signals (DMRS)-based channel estimation, and Effective Exponential SINR Mapping (EESM) aggregation—demonstrate rapid convergence for critical services under fully decentralized execution. 5G NR-V2X V2V Mode 2 continuous resource allocation Actor–Critic CTDE multi-agent reinforcement learning Full Text Cite Share Download PDF Status: Published Journal Publication published 15 Apr, 2026 Read the published version in Journal on Wireless Communications and Networking → 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. 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