AI 中文总结
研究蜂窝车联网无线接入技术选择决策问题，用多智能体强化学习算法MAPPO解决，与五个基线比较。在城市场景及通信用例中评估，结果显示按时交付率提高，训练时间减半，表明自适应通信策略有益，多智能体建模可解决决策问题。
AI 中文摘要
车辆越来越多地配备了先进的车联网通信能力。早期车联网应用使用诸如协作感知消息等服务，近期发展使得包括协作驾驶、共享感知和传感器共享服务等更先进的应用成为可能。应用的多样化导致对延迟和可靠性有异构需求。同时，多种车联网通信技术各有利弊。混合车联网通信可适时利用不同优势来满足应用需求。本文研究蜂窝Uu链路、NR-V2X PC5侧链路以及两者同时使用之间的决策问题。通过使用多智能体强化学习算法MAPPO来解决此问题，并与由深度强化学习方法、静态决策树方法和静态信道选择策略组成的五个基线进行比较。在城市场景和一组选定的通信用例中对这些方法进行评估。评估结果表明，与深度强化学习方法相比，在单控车辆设置中，按时交付率从0.508提高到0.535，当所有车辆遵循学习到的策略时，从0.548提高到0.567，且训练时间减少一半。收益主要来自先进应用场景，而非仅涉及协作感知消息的场景。这表明未来应用将受益于这种自适应通信策略，且多智能体建模对于解决潜在决策问题很有用。
英文摘要
Vehicles are increasingly equipped with advanced V2X communication capabilities. While early V2X apps utilized services such as Cooperative Awareness Messages, recent developments have allowed more advanced applications including cooperative driving, shared perception, and sensor-sharing services. The broader mix of applications leads to heterogeneous requirements for latency and reliability. At the same time multiple communication technologies for V2X are available with pros and cons. Hybrid V2X communication can exploit the distinct advantages at the right moment to fulfill the requirements of the applications. This work studies the decision problem between cellular Uu link, NR-V2X PC5 sidelink, and the simultaneous use of both channels. We address this problem by using the multi-agent reinforcement learning algorithm MAPPO and compare it to five baselines consisting of a deep reinforcement learning (DRL) approach, a static decision tree approach and static channel selection strategies. The methods are evaluated in an urban scenario and with a set of selected communication use cases. The evaluation results show that when compared to the DRL approach, the on-time delivery ratio improves from 0.508 to 0.535 in a single-controlled-vehicle setting and from 0.548 to 0.567 when all vehicles follow the learned policy and reduces the training time by half. The gains result mainly from the advanced applications scenarios, as opposed to scenarios involving exclusively CAM messaging. This indicates future applications will benefit from such adaptive communication strategies and that multi-agent modelling is useful for addressing the underlying decision problem.
