Joint mode selection and resource allocation for cellular V2X communication using distributed deep reinforcement learning under 5G and beyond networks

Abstract

Vehicle-to-everything (V2X) communication via cellular networks is a promising technique for 5G and beyond networks. The cars interact directly with one another, as well as with the infrastructure and various vehicles on the road, in this mode. It enables the interchange of time-sensitive and safety-critical data. Despite these benefits, unstable vehicle-to-vehicle (V2V) communications, insufficient channel status information, high transmission overhead, and the considerable communication cost of centralized resource allocation systems all pose challenges for defense applications. To address these difficulties, this study proposes a combined mode selection and resource allocation system based on distributed deep reinforcement learning (DRL) to optimize the overall network sum rate while maintaining the reliability and latency requirements of V2V pairs and the data rate of V2R connections. Because the optimization issue is non-convex and NP-hard, it can’t be solved directly. To tackle this problem, the defined problem is first translated into machine learning form using the Markov decision process (MDP) to construct the reward function and decide whether agent would conduct the action. Following that, the distributed coordinated duelling deep Q-network (DDQN) method based on prioritized sampling is employed to improve mode selection and resource allocation. This approach learns the action-value distribution by estimating both the state-value and action advantage functions using duelling deep networks. The results of the simulation show that the suggested scheme outperforms state-of-the-art decentralized systems in terms of sum rate and QoS satisfaction probability.