Adaptive Channel Estimation and Tracking for URA-Based Massive MIMO Systems

Abstract

In this paper, an adaptive channel estimation and tracking method is proposed for the uniform rectangular array (URA) based massive multiple-input and multiple-output (M-MIMO) systems. To decrease the computational complexity while tracking the change on the channel state information (CSI) in time, a principal subspace analysis (PSA) based scheme is proposed to estimate and track the signal subspace based on the received signal covariance matrix. Based on the estimated signal subspace, the channel parameters including the direction of arrival (DoA) angle and the channel gain corresponding to each resolvable path of the channel, can be recovered via the classic multiple signal classification (MUSIC) algorithm. Then, the Cramer-Rao lower bound (CRLB) is derived to evaluate the performance of the proposed scheme. Simulation results are provided to verify the accuracy of the proposed scheme on the channel estimation.