Sparsity Adaptive Compressive Sensing based Two-stage Channel Estimation Algorithm for Massive MIMO-OFDM Systems

Abstract

Massive multi-input multioutput (MIMO) coupled with orthogonal frequency division multiplexing (OFDM) has been utilized extensively in wireless communication systems to investigate spatial diversity. However, the increasing need for channel estimate pilots greatly increases spectrum consumption and signal overhead in massive MIMO-OFDM systems. This paper proposes a two-stage channel estimation algorithm based on sparsity adaptive compressive sensing (CS) to address this issue. To estimate the channel state information (CSI) for pilot locations in Stage 1, we provide a geometry mean-based block orthogonal matching pursuit (GBMP) method. By calculating the geometric mean of the energy in the support set of the channel response, the GBMP method, when compared to conventional CS methods, can drastically reduce the number of iterations and effectively increase the convergence rate of channel reconstruction. Stage 2 involves estimating the CSI for nonpilot locations using a time-frequency correlation interpolation method, which can increase the accuracy of the channel estimation and is dependent on the estimated results from Stage 1. According to the simulation results, the proposed two-stage channel estimation algorithm greatly reduces the running time with little error performance degradation when compared to traditional channel estimating algorithms.