College of Information Science and Engineering, Xinjiang University, Urumqi 830000, China.
Network Department, China Mobile Communications Group Xinjiang Co., Ltd., Urumqi 830000, China.
Sensors (Basel). 2022 Feb 27;22(5):1874. doi: 10.3390/s22051874.
Vehicle-to-vehicle (V2V) communication has attracted increasing attention since it can improve road safety and traffic efficiency. In the underlay approach of mode 3, the V2V links need to reuse the spectrum resources preoccupied with vehicle-to-infrastructure (V2I) links, which will interfere with the V2I links. Therefore, how to allocate wireless resources flexibly and improve the throughput of the V2I links while meeting the low latency requirements of the V2V links needs to be determined. This paper proposes a V2V resource allocation framework based on deep reinforcement learning. The base station (BS) uses a double deep Q network to allocate resources intelligently. In particular, to reduce the signaling overhead for the BS to acquire channel state information (CSI) in mode 3, the BS optimizes the resource allocation strategy based on partial CSI in the framework of this article. The simulation results indicate that the proposed scheme can meet the low latency requirements of V2V links while increasing the capacity of the V2I links compared with the other methods. In addition, the proposed partial CSI design has comparable performance to complete CSI.
车对车（V2V）通信自吸引关注以来，它可以提高道路安全和交通效率。在模式 3 的覆盖方法中，V2V 链路需要重用被车辆到基础设施（V2I）链路占用的频谱资源，这将干扰 V2I 链路。因此，需要确定如何灵活分配无线资源并提高 V2I 链路的吞吐量，同时满足 V2V 链路的低延迟要求。本文提出了一种基于深度强化学习的 V2V 资源分配框架。基站（BS）使用双深度 Q 网络智能地分配资源。特别是，为了减少模式 3 中 BS 获取信道状态信息（CSI）的信令开销，BS 在本文框架中基于部分 CSI 优化资源分配策略。仿真结果表明，与其他方法相比，所提出的方案可以满足 V2V 链路的低延迟要求，同时增加 V2I 链路的容量。此外，所提出的部分 CSI 设计与完整 CSI 具有可比的性能。
