Kalman-filter based hierarchical channel estimation for RIS-aided mmWave communication systems

Abstract

Reconfigurable intelligent surface (RIS) technology has emerged as a promising solution to enhance network coverage and spectral efficiency in mmWave communication systems, where the RIS-aided localization has attracted much attention for its capability of simultaneously obtaining two geographically separated copies of steering vectors of users at one base station (BS). However, the estimation of the cascaded channel parameters of RISs still remains a challenging problem due to the passive nature of RISs and the large number of channel links between the BS and RIS. To tackle this issue, we propose to jointly employ the multiple signal classification (MUSIC) algorithm and the Kalman filter (KF) to hierarchically estimate the azimuth angle and the path loss with the help of an anchor node. The simulation results indicate that the proposed method is significantly superior to the benchmark technique in terms of accuracy and computational complexity, and reduces the pilot frequency overhead by over 40%.