CKM-Based Environment-Aware Codebook Design for RIS-Assisted mmWave Communications With Hybrid Beamforming

In this paper, a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication system with hybrid beamforming is considered, where the base station (BS) transmits signals to users via the RIS. Due to the difficulty in acquiring accurate channel state information (CSI) and the high complexity of hybrid beamforming design, a codebook-based method is employed to balance communication performance and computational cost. However, conventional codebook construction typically neglects the influence of surrounding communication environments, resulting in poor adaptability in complex mmWave scenarios. Therefore, we propose a three-stage environment-aware codebook design framework based on channel knowledge map (CKM) to enhance the adaptability of systems under different channel conditions. Specifically, in the first stage, a deep learning (DL)-based method is proposed to construct the CKM, which is a site-specific database that provides accurate CSI for given locations. In the second stage, the CSI of potential users is rapidly acquired based on the CKM, then a deep reinforcement learning (DRL) algorithm is employed to generate environment-aware codebooks by solving a sum achievable rate maximization problem. In the third stage, the end-to-end composite channel is first estimated. Then, the digital beamforming is computed by using the minimum mean square error (MMSE) criterion. Finally, the optimal analog beamforming and RIS phase shifts are selected through codebook scanning. Simulation results demonstrate that the proposed CKM-based environment-aware codebooks significantly outperform other codebook schemes in terms of sum achievable rate.