Study of a dual RIS-assisted beam assignment algorithm in a high-speed moving scenario

Abstract

In order to improve the frequent Doppler bias in the high-speed mobile scenario, improve the communication quality on HSR (High-Speed Railway). An algorithm can improve communication performance through RIS (intelligent metasurface) phase optimization. The optimization process is modeled as a reinforcement learning task, where the agent in the channel learns to select the optimal RIS phase by interacting with the environment to reduce the effect of Doppler shift.The model covers characteristic parameters such as signal propagation path and frequency related to RIS, and the agent influences the beam direction by adjusting the phase, and learns the optimal phase configuration in the high-speed moving scene.The RIS phase optimization method implemented based on the deep reinforcement learning algorithm has high convergence and has significant advantages in improving the Doppler frequency shift problem. Compared with the traditional frequency bias estimation method, this method can significantly improve the transmission performance. The effectiveness of the proposed algorithm is verified by performing large-scale training and evaluation in the simulation environment.