Efficient Multi-user Channel Estimation for RIS-aided mmWave Systems using Shared Channel Subspace

Abstract

This paper presents an efficient channel estimation algorithm for multi-user reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) systems. In this paper, the concept of low rank matrix completion (LRMC) is exploited to reduce beam training overhead for channel estimation. The proposed beam training samples part of each channel matrix in a special pattern that is suitable for LRMC with less beam training overhead. Then, the beam training is followed by multi-user channel estimation. For computationally efficient channel estimation, the proposed algorithm exploits the property that all the channel matrices share the same low-rank subspace in multi-user RIS-aided systems. The shared subspace is derived by combining candidate subspaces, which are estimated by fast alternating least squares (FALS) from partially observed channels. With the shared subspace, all the missing entries of channels are recovered via computationally efficient linear estimation. The simulations and complexity analysis demonstrate that the proposed algorithm shows a superior accuracy-complexity trade-off compared to existing works.