Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking
arXiv:2603.06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications. Multi-agent reinforcement learning (MARL) has emerged as a promising approach for this problem. However, key MARL challenges, including non-stationarity, coordination difficulty, large action space, partial observability, and limited robustness and generalization, are often intertwined, making it difficult to assess their individual impa
The increasing complexity and demands of C-V2X networks necessitate more sophisticated resource allocation methods, pushing research towards AI-driven solutions like MARL.
Efficient and reliable radio resource allocation (RRA) is crucial for the safety and functionality of future connected vehicle ecosystems, impacting transportation, logistics, and smart city infrastructure.
The explicit identification and benchmarking of persistent MARL challenges for C-V2X RRA provides a clearer roadmap for future development and deployment of autonomous communication systems.
- · AI/ML researchers and developers
- · Telecommunications companies
- · Automotive industry
- · Smart city developers
- · Traditional RRA methods
- · Legacy cellular network providers
Improved efficiency and reliability of C-V2X communications for safety-critical applications.
Accelerated adoption and deployment of autonomous vehicles and intelligent transportation systems due to enhanced underlying communication infrastructure.
Reduced traffic congestion and accidents, alongside new mobility-as-a-service paradigms enabled by ultra-reliable and low-latency C-V2X.
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Read at arXiv cs.AI