Knowledge Base Autoencoder Framework: A Novel Approach for Continuous Phase Shift Compression in RIS-Aided Communications

In a communication system assisted by reconfigurable intelligent surfaces (RIS), phase optimization on RIS is not feasible by itself because of its passive nature. Therefore, the base station (BS) must convey the optimized phase shift to RIS via a dedicated control channel. However, the large number of RIS unit cells results in a considerable number of phase shift parameters, occupying a substantial amount of signaling resources. To address this challenge, this paper proposes a novel knowledge base autoencoder (KBAE) framework for compressing continuous phase shifts in RIS-aided communications. The KBAE framework integrates a specially designed phase shift compression network (PSCNet) and a higher compression ratio version (PSCNet-H) to achieve high-efficiency phase shift compression. The scheme utilizes a learnable knowledge base to approximate the distribution of RIS phase shift features. It only needs to transmit the indexes of the vectors in the knowledge base that are most similar to the RIS phase shift feature. Simulation results demonstrated that the proposed scheme can significantly improve the reconstruction accuracy of RIS phase shift and the average achievable rate compared to the benchmark methods. These advancements highlight the potential of KBAE to enhance the scalability and performance of future wireless networks.