Effective Beamforming Design Using DL-Based Codebook Classification in RIS-Aided mmWave Systems

Reconfigurable intelligent surface (RIS) is highly envisaged as a promising technology in millimeter wave (mmWave) communication systems for its capability of restructuring the wireless communication environment and mitigating the severe signal blockage. However, passive RIS beamforming design is still a challenge due to the non-convex property of the problem. This work presents a deep learning (DL) based codebook classification beam search algorithm, comprising a convolutional neural network (CNN) based codebook searcher and a mapper. The searcher determines the optimal codeword position range index based on the channel state information (CSI), while the mapper selects the optimal codeword based on this index. The proposed network is trained both with the DeepMIMO dataset and the Saleh-Valenzuela channel model, respectively, and imperfect CSI is utilized to enhance the system robustness. Simulation results demonstrate that the proposed algorithm significantly improves the beam search efficiency.