A 5G network is the key driving factor in the development of vehicle-to-vehicle (V2V) communication technology, and V2V communication in 5G has recently attracted great interest. In the V2V communication network, users can choose different transmission modes and power levels for communication, to guarantee their quality-of-service (QoS), high capacity of vehicle-to-infrastructure (V2I) links and ultra-reliability of V2Vlinks. Aiming atV2V communication mode selection and power adaptation in 5G communication networks, a reinforcement learning (RL) framework based on slow fading parameters and statistical information is proposed. In this paper, our objective is to maximize the total capacity of V2I links while guaranteeing the strict transmission delay and reliability constraints of V2V links. Considering the fast channel variations and the continuous-valued state in a high mobility vehicular environment, we use a multi-agent double deep Q-learning (DDQN) algorithm. Each V2V link is considered as an agent, learning the optimal policy with the updated Q-network by interacting with the environment. Experiments verify the convergence of our algorithm. The simulation results show that the proposed scheme can significantly optimize the total capacity of the V2I links and ensure the latency and reliability requirements of the V2V links.

中文翻译：

---

一种5G中V2V通信网络联合模式选择和功率自适应的强化学习方法

5G网络是车对车（V2V）通信技术发展的关键驱动因素，5G中的V2V通信最近引起了极大的兴趣。在V2V通信网络中，用户可以选择不同的传输模式和功率等级进行通信，以保证其服务质量（QoS）、车对基础设施（V2I）链路的高容量和V2Vlinks的超可靠性。针对5G通信网络中V2V通信模式选择和功率自适应，提出了一种基于慢衰落参数和统计信息的强化学习（RL）框架。在本文中，我们的目标是在保证严格的 V2V 链路传输延迟和可靠性约束的同时，最大化 V2I 链路的总容量。考虑到高移动性车辆环境中的快速通道变化和连续值状态，我们使用多智能体双深度 Q 学习（DDQN）算法。每个 V2V 链接都被视为一个代理，通过与环境交互来学习更新的 Q 网络的最佳策略。实验验证了我们算法的收敛性。仿真结果表明，该方案能够显着优化V2I链路的总容量，保证V2V链路的时延和可靠性要求。