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In reconfigurable intelligent surface (RIS)-assisted wireless communicationsystems, adjusting the phase shift of RIS unit cells is crucial for improvingcommunication performance. Due to massive RIS unit cells, the number of phaseshift 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 proposea feedback scheme for millimeter-wave RIS phase shift applying a knowledge baseautoencoder framework, in which the learnable knowledge base is shared at theBS and the RIS. The encoder at the BS compresses the RIS phase shift matrix tomultiple feature vectors. Then the knowledge base vectors index is obtained bycalculating the similarity between feature vectors and knowledge base vectorsand transmitted to the RIS. With utilizing the index at the RIS, thecorresponding knowledge base vectors are extracted and used as the decoder'sinputs to reconstruct the phase shift of the RIS. Simulation results show thatthe proposed scheme can significantly improve the accuracy of phase shiftfeedback and impressively reduce the amount of RIS phase shift feedback data.Moreover, the proposed scheme is easy to deploy in actual scenarios due tolower complexity and fewer parameters.

mmWave RIS Phase Shift Feedback Based on Knowledge Base Autoencoder Framework