Channel Estimation for RIS-Empowered Multi-User MISO Wireless Communications

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity low-latency communications. Accurate low-overhead channel estimation RIS-based systems is one of the most critical challenges usually large number RIS unit elements distinctive hardware constraints. In this paper, we focus on uplink a RIS-empowered multi-user Multiple Input Single Output (MISO) communication propose framework based parallel factor decomposition unfold resulting cascaded model. We present two iterative algorithms channels between base station RIS, well users. One alternating least squares (ALS), while other uses vector approximate message passing iteratively reconstruct unknown from estimated vectors. To theoretically assess performance ALS-based algorithm, derived its Cramér-Rao Bound (CRB). also discuss downlink achievable sum rate computation with different precoding schemes station. Our extensive simulation results show that our outperform benchmark ALS technique achieves CRB. It demonstrated using always reach perfect under various settings, thus, verifying effectiveness robustness proposed algorithms.