User Schedule and Single-User RIS Allocation in QoS-Aware MmWave Vehicular Networks

The combination of millimeter-wave (mmWave) and massive MIMO techniques can fulfill high data rate requirements for vehicular networks. However, due to the elevated path loss and severe blocking effects in mmWave propagation, the downlink data service of vehicles will seriously deteriorate. Fortunately, reconfigurable intelligent surface (RIS) can serve as a single-user relay to mitigate individual performance degradation without additional power consumption. In this paper, we propose a deep reinforcement learning (DRL)-based joint user schedule and RIS-User pairing scheme for the dynamic mmWave vehicular network to alleviate the blocking effects and maximize the total transmission data volume while ensuring the quality of service (QoS) for all target vehicles. In this scheme, each target vehicle has a distinct QoS constraint called minimum service data volume, which is a long-term and posterior optimization problem. Thus, QoS constraints are introduced in the reward design of the DRL algorithm, and the problem of high-dimensional action spaces is addressed by utilizing two nested Dueling Double-DQN (D-D3QN) networks. Simulation results demonstrate the superiority of our scheme in mmWave vehicular networks.