Deep Reinforcement Learning-aided Transmission Design for Multi-user V2V Networks

Intelligent connected vehicle (ICV) has been widely deemed as the key to reduce road accident rate and improve traffic efficiency. However, ensuring high communication reliability and low transmission delay in vehicular networks is challenging, especially in large-scale dynamic networks with diverse heterogeneous data exchange demands. In this paper, we investigate the potential of applying the deep reinforcement learning (DRL) technique to facilitate efficient transmission design in a class of complex multi-user vehicle-to-vehicle (V2V) networks, where conventional mathematical tools confront difficulties in solving the design optimization problems. The considered network contains several pairs of V2V links sharing the channel resource. Each link desires to communicate two types of delay-sensitive messages to support different safety-related applications with the maximum energy efficiency. We propose transforming the power/rate control problem into a Markov decision process and then solving it using the deep deterministic policy gradient (DDPG) algorithm. Simulation results show that in a two-user network our DRL-aided solution can achieve better performance than that with Lyapunov optimization. Extending the former to work in a larger network is straightforward, but it is not the case for the latter. The advantages of applying DRL to support wireless system design are thus demonstrated.