Platoon Leader Selection, User Association and Resource Allocation on a C-V2X Based Highway: A Reinforcement Learning Approach
Author:
Affiliation:
1. McGill University,Department of Electrical & Computer Engineering,Montréal,QC,Canada
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10278505/10278554/10279056.pdf?arnumber=10279056
Reference16 articles.
1. AoI-aware resource allocation for platoon-based C-V2X networks via multi-agent multi-task reinforcement learning;parvini;ArXiv Preprint,2021
2. Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems
3. Human-level control through deep reinforcement learning;mnih;Nature,2015
4. Resource Allocation for Vehicle Platooning in 5G NR-V2X via Deep Reinforcement Learning
5. Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning
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1. Enhancing C-V2X Vehicle Platooning: A Dual-Strategy of Time Synchronisation and Partition-Based Scheduling;2024 IEEE International Symposium on Measurements & Networking (M&N);2024-07-02
2. Resource allocation in V2X communication: State-of-the-art and research challenges;Physical Communication;2024-06
3. Resource allocation optimization for effective vehicle network communications using multi-agent deep reinforcement learning;Journal of Dynamics and Games;2024
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