AI 中文总结
该研究针对6G车联网V2X通信的干扰与资源问题，提出O-RAN辅助的MARL系统，采用聚类与集中训练分布式执行策略，在仅车辆及VRU共存场景下均显著降低损失与延迟。
AI 中文摘要
基于6G的车联网未来应用将利用车万物（V2X）场景中的侧链路（SL）传输。然而，基于SL的直接通信会显著增加车辆之间、车辆与智能交通系统其他实体之间的干扰，导致车对车通信以及弱势道路使用者（VRU）的上行链路资源可能退化或出现资源匮乏问题。现有解决方案主要聚焦于优化资源分配与配对选择，但缺乏应对通信模式与整个网络的综合方法。为解决这些挑战，本文利用开放无线接入网（Open RAN）管理V2X通信，提出一种感知资源的多智能体强化学习（MARL）系统。Open RAN通过网络全局视图提供控制环路，还提供基于开放接口的框架，供机器学习模型用于资源决策。同时，MARL模型旨在通过在侧链路传输与网络传输间进行最优选择，以减轻干扰、优化资源使用并提升服务质量。为降低系统复杂度，本研究采用聚类策略，每个智能体管理一组配对，而非为每个配对分配一个智能体，该方案依托Open RAN采用集中式训练与分布式执行的设计，策略采用离线训练与离策略方法，每个智能体存储经验用于微调。结果表明，仅车辆场景下，该MARL方法使平均损失降低21%，延迟降低19%；在VRU共存场景下，与单智能体方法相比，损失降低18%，延迟降低30%。
英文摘要
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.
CommentsThis paper has been accepted for publication in IEEE Transactions on Vehicular Technology
