Joint Resource Allocation and String-Stable CACC Design with Multi-Agent Reinforcement Learning
Author:
Affiliation:
1. Technische Universität Dresden,Vodafone Chair Mobile Communications Systems,Germany
2. Vodafone Tech Innovation Center Dresden,Germany
Funder
Federal Ministry of Education and Research
German Research Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10278505/10278554/10279834.pdf?arnumber=10279834
Reference17 articles.
1. Resource Allocation for D2D-Enabled Vehicular Communications
2. Deep Reinforcement Learning Aided Platoon Control Relying on V2X Information
3. Multi-Agent Deep Reinforcement Learning for Cooperative Offloading in Cloud-Edge Computing
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1. Message Interval Control for Efficient Resource Utilization in Dynamic Platoons Using Multi-Agent Reinforcement Learning;2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC);2024-06-28
2. Resource allocation optimization for effective vehicle network communications using multi-agent deep reinforcement learning;Journal of Dynamics and Games;2024
3. Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based Solutions;IEEE Open Journal of the Communications Society;2024
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