Second-Order Statistics-Aided Channel Estimation for Multipath Massive MIMO-OFDM Systems

Abstract

This paper develops an efficient channel estimation algorithm based on second-order statistics of time division duplex (TDD) multiuser massive multiple-input multiple-output (MIMO) systems. The algorithm uses the received signal correlation to determine the most significant lags (MSLs) of the received signal. We first employ these MSLs to propose a novel set containing the channel’s four most significant taps (MSTs). Then, by using them, we propose an efficient semi-blind iterative algorithm called enhanced modified-subspace pursuit (EM-SP). It uses the set mentioned above and two theoretical results (Lemma 1 and Theorem 1) to estimate an arbitrary number of MSTs efficiently. Simulation results show that the normalized mean square error (NMSE) of the proposed EM-SP algorithm is much smaller than that of the subspace pursuit (SP) and orthogonal matching pursuit (OMP) algorithms at the cost of 0.3 % and 2 % more computational complexity for the channels with three and six nonzero paths, respectively. Moreover, the NMSE of it is very close to that of the optimal genie-aided least square algorithm.

Keywords

• Compressive sensing
• massive MIMO-OFDM
• frequency-selective channel estimation
• most significant tap (MST) detection
• second-order statistics