  In reconfigurable intelligent surface (RIS)-assisted wireless communication
systems, adjusting the phase shift of RIS unit cells is crucial for improving
communication performance. Due to massive RIS unit cells, the number of phase
shift parameters fed back from the base station (BS) to the RIS is enormous,
which occupies a large number of frequency resources. In this paper, we propose
a feedback scheme for millimeter-wave RIS phase shift applying a knowledge base
autoencoder framework, in which the learnable knowledge base is shared at the
BS and the RIS. The encoder at the BS compresses the RIS phase shift matrix to
multiple feature vectors. Then the knowledge base vectors index is obtained by
calculating the similarity between feature vectors and knowledge base vectors
and transmitted to the RIS. With utilizing the index at the RIS, the
corresponding knowledge base vectors are extracted and used as the decoder's
inputs to reconstruct the phase shift of the RIS. Simulation results show that
the proposed scheme can significantly improve the accuracy of phase shift
feedback and impressively reduce the amount of RIS phase shift feedback data.
Moreover, the proposed scheme is easy to deploy in actual scenarios due to
lower complexity and fewer parameters.
