Kalman Filter-based Mobile User-RIS Channel Estimation and User Localization

TL;DR

This paper presents a Kalman Filter-based method for improved channel estimation and user localization in RIS-assisted wireless systems, especially under high mobility and challenging noise conditions.

Contribution

It introduces a novel Kalman Filter approach, including a Non-Circular Noise variant, for enhanced channel estimation and localization in dynamic RIS environments.

Findings

KF achieves lower MSE than existing methods

NCNKF performs better with non-circular noise

DSFT interpolation reduces localization RMSE

Abstract

In communication networks, channel estimation and user localization are challenging problems in harsh environments or signal-blocked areas. This paper introduces a novel approach to minimize the Mean Squared Error (MSE) in channel estimation between mobile users and rectangular Reconfigurable Intelligent Surfaces (RIS) within wireless communication systems. Meanwhile, the user localization is realized based on the estimated Channel State Information (CSI). In this paper, we assume a non-linear, user's position-dependent system model, for a user with high mobility, an RIS with multiple elements, and a base station (BS) with multiple antennas. After that, we apply the Kalman Filtering (KF) like algorithms to reduce MSE in estimating parameters of this time-variant channel model. Additionally, we propose a Non-Circular Noise Kalman Filter (NCNKF) to address scenarios with non-circular complex state-space noise. Furthermore, we apply the Discrete Space Fourier Transform (DSFT) method, combined with the interpolation techniques to decrease the Root Mean Squared Error (RMSE) for the user localization based on the estimated CSI. Finally, we extend the single-user case into the multi-user situation. Results show that KF can achieve lower MSE in estimating the channel than other known approaches, while the NCNKF algorithm has better performance in non-circular state-space noise scenarios. At the same time, the DSFT interpolation outperforms the other approaches with lower complexity. The study concludes with numerical comparisons and an in-depth discussion of the performance improvements enabled by our approaches.