DQL-based intelligent scheduling algorithm for automatic driving in massive MIMO V2I scenarios

Connected and autonomous vehicle (CAV) vehicle to infrastructure (V2I) scenarios have more stringent requirements on the communication rate, delay, and reliability of the Internet of vehicles (IoV). New radio vehicle to everything (NR-V2X) adopts link adaptation (LA) to improve the efficiency and reliability of road safety information transmission. In order to solve the problem that the existing LA scheduling algorithms cannot adapt to the Doppler shift and complex fast time-varying channel in V2I scenario, resulting in low reliability of information transmission, this paper proposes a deep Q-learning (DQL)-based massive multiple-input multiple-output (MIMO) LA scheduling algorithm for autonomous driving V2I scenario. The algorithm combines deep neural network (DNN) with Q-learning (QL) algorithm, which is used for joint scheduling of modulation and coding scheme (MCS) and space division multiplexing (SDM). The system simulation results show that the algorithm proposed in this paper can fully adapt to the different channel environment in the V2I scenario, and select the optimal MCS and SDM for the transmission of road safety information, thereby the accuracy of road safety information transmission is improved, collision accidents can be avoided, and bring a good autonomous driving experience.