Joint Channel Estimation and Localization in RIS Assisted OFDM-MIMO System

Reconfigurable Intelligent Surfaces (RISs) are expected to become one of the enabling technologies in the development of the sixth generation (6G) smart radio environment, where services such as localization and sensing are going to be ubiquitous and an integral part of the system. However, achieving localization in a dense urban environment is a challenging task. Therefore, this study considers joint channel and localization estimation in a RIS-assisted orthogonal frequency division multiplexing multiple-input multiple-output (OFDM-MIMO) system operating in a dense urban environment, where only Non-Line-of-Sight (NLoS) multipath components are available. The channel estimation is based on the channels being sparse in the spatial and angular domains and having only a few Angles of Arrival (AoA) and Angles of Departure (AoD), thus the channel estimation becomes a sparse signal recovery problem that can be solved with compressive sensing algorithms. The study implements Simultaneous Orthogonal Matching Pursuit (SOMP) to obtain AoA and AoD. The time delay parameters are obtained by correlating OFDM subcarriers with a search matrix. A localization estimation algorithm based on linearization of the estimated geometric parameters is adopted. The results of the study show that for a channel consisting of 3 NLoS paths, location estimation in the range of decimeters is possible.