Pilot Allocation for Distributed-Compressed-Sensing-Based Sparse Channel Estimation in MIMO-OFDM Systems

This paper addresses the sparse channel estimation problem in multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems from the perspective of distributed compressed sensing (DCS). It is focused on deterministic pilot allocation of MIMO-OFDM systems to improve the performance of DCS-based channel estimation. By transforming the problem of DCS-based channel estimation to a problem of reconstructing block-sparse signals, a class of mutual coherence-related criteria is first proposed for optimizing pilot locations. By employing the proposed criteria, a genetic algorithm (GA)-based method of optimizing the pilot locations is then presented. Simulation results show that the DCS-based MIMO channel estimation with optimized pilot locations can improve the spectrum efficiency by nearly 36% and the bit error rate (BER) performance by 1.5 dB, as compared with the least squares (LS) channel estimation with equidistant pilot locations. Moreover, the DCS-based MIMO channel estimation yields a 4.7% improvement in spectrum efficiency under the same BER performance over the compressed sensing (CS)-based channel estimation.