Problem
Research questions and friction points this paper is trying to address.
V2X Communications
Sidelink Interference
Resource Allocation
Open RAN
Multi-Agent Reinforcement Learning
An O-RAN-Assisted MARL Approach for Dynamic Sidelink and Infrastructure Selection in V2X Communications
🤖 AI Summary
本文针对V2X通信中侧链路和基础设施选择问题，利用O-RAN支持的多智能体强化学习方法优化资源使用并减少干扰。
📝 Abstract
Future applications in the 6G-based Internet of Vehicles will leverage sidelink (SL) transmissions in Vehicle-to-Everything (V2X) scenarios. However, SL-based direct communication can significantly increase interference among vehicles and between vehicles and other entities of the Intelligent Transportation System. Thus, both Vehicle-to-Vehicle communications and Vulnerable Road Users (VRUs) uplink resources may be degraded or subject to starvation. Existing solutions primarily focus on improving resource allocation and pair selection. Nonetheless, they lack a comprehensive approach to tackle the communication modes and the entire network. To address these challenges, this paper leverages Open RAN to manage V2X communication and proposes a multi-agent reinforcement learning (MARL) resource-aware system. Open RAN provides control loops through a global view of the network and also an open interface-based framework for machine learning models applied to resource decision-making. Meanwhile, the MARL model aims to mitigate interference, optimize resource usage, and enhance quality of service by optimally selecting between sidelink and network transmissions. To reduce system complexity, this work employs a clustering strategy. Each agent manages a group of pairs, rather than assigning one agent to each pair. The solution supports this design by adopting a centralized training with decentralized execution approach, empowered by Open RAN. The strategy uses offline training and an off-policy approach, in which each agent stores experience for fine-tuning. Results indicate that the MARL approach reduces average loss by 21% and latency by 19% in Vehicle-only scenarios. In coexistence VRU scenarios, loss and latency drop by 18% and 30%, respectively, compared to the single-agent approach.
Innovation
Methods, ideas, or system contributions that make the work stand out.
O-RAN
Multi-Agent Reinforcement Learning (MARL)
Dynamic Sidelink and Infrastructure Selection
Clustering Strategy
Centralized Training with Decentralized Execution
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