Mobile User-RIS Channel Estimation Using Kalman Filter based Approaches

This paper introduces a novel approach to minimizing the Mean Squared Error (MSE) in channel estimation between mobile users and Reconfigurable Intelligent Surfaces (RIS) within wireless communication systems. Our model includes a mobile user, an RIS with multiple elements, and a base station (BS) with multiple antennas. We apply the Kalman Filter (KF) and Extended Kalman Filter (EKF) like algorithms to reduce MSE in a non-linear, distance-dependent channel model. Additionally, we propose a Non-Circular Noise Kalman Filter (NCNKF) to address scenarios with non-circular complex state-space noise. Results show that KF and EKF can achieve lower MSE in estimating the channel than other known approaches, while the NCNKF algorithm outperforms others in non-circular state-space noise environments. The study concludes with numerical comparisons and an in-depth discussion of the performance improvements enabled by our approach.