Intelligent Reflector Channel Estimation Scheme Based on Antenna Selection

Abstract

An intelligent reflective surface (IRS) comprises numerous intelligent reflective elements made of passive materials. This can change the propagation environment of the signals in wireless channels. With the development of intelligent reflector theory, channel estimation based on IRS has become a critical task. This study uses a Bayesian estimator to estimate massive multiple-input multiple-output (MIMO) channels based on intelligent-reflector-assisted conditions. First, a reflection phase shift selection scheme for maximizing the line-of-sight (LOS) intelligent reflector channel strength is proposed. This scheme utilizes the orthogonal characteristics of the channel to reduce channel interference. Second, this study provides three antenna selection schemes for the uplink with the assistance of an intelligent reflector. Solution 1 is to maximizemaximise the strength of the intelligent reflective surface line of sight channel and obtain closed form solutions in the algorithm via QR decomposition. Solution 2 is to maximize the strength of the cascaded channels to solve the problem of obtaining only suboptimal solutions in solution 1. Solution 3 is to maximize the channel capacity to further improve the spectral efficiency of the system. Finally, the intelligent phase shift under antenna selection is optimized, and the channel is estimated based on the coupling characteristics of the base station antenna and the reflection phase shift. The proposed methods exploit the spatial correlation characteristics at both the base station and planar IRSs as well as other statistical characteristics of multi-specular fading in a mobile environment. The simulation results show that compared with channel estimation in large-scale MIMO systems, the proposed channel estimation framework based on antenna selection improves the uplink spectrum efficiency of mobile user devices at the edge of a cell with the assistance of the uplink IRS. We simulated and compared channel estimation methods based on antenna selection with non-antenna selection channel estimation methods to obtain optimal solutions under different conditions.