Semi-Blind Channel Estimation and Pilot Allocation for Doubly-Selective Massive MIMO-OFDM Systems

Abstract

This article addresses the problem of channel estimation and pilot allocation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) networks for sparse doubly selective (DS) channels. In DS channels, due to significant Doppler shifts, channels vary fast, a huge number of coefficients must be estimated, and inter-carrier interference (ICI) must be overcome, which make the problem very challenging. To address these challenges, we reframe the issue as a distributed compressive sensing (DCS) coefficient estimation problem within the basis expansion model (BEM) for channel representation. This reduces the number of channel coefficients significantly as in the new problem, the jointly sparse BEM coefficients are estimated. To further improve estimation performance, we first employ a genetic algorithm to optimize pilot locations based on the mutual coherence of the measurement matrix as the criterion. Secondly, taking the advantage of the complex exponential (CE)-BEM, the correlation of the received signal is used to estimate the channel’s most significant lags (MSLs). Using optimized pilot locations and MSLs, we propose a novel algorithm, called semi-blind block orthogonal matching pursuit (SB-BOMP) for channel estimation. Extensive simulation results confirm that the proposed algorithm outperforms the traditional ones over DS channels.

Keywords

• Massive MIMO-OFDM
• block distributed compressive sensing (BDCS)
• doubly selective (DS) channels
• pilot allocation
• semi-blind channel estimation