BSBL-based Block-Sparse Channel Estimation for Affine Precoded OSTBC MIMO-OFDM Systems

Abstract

This work presents affine precoded superimposed pilot-based sparse channel estimation in orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. A pilot-based block sparse Bayesian learning (P-BSBL) technique is developed initially, which leverages the sparsity as well as the spatial correlation of the MIMO channel for improved estimation. Subsequently, a data aided-BSBL (D-BSBL) technique is presented for joint maximum likelihood (ML) decoding of the symbols and sparse channel estimation, which is shown to lead to a further improvement in the accuracy of the estimated channel. In addition to a significant decrease in the mean squared error (MSE) of estimation, the proposed schemes are also shown to lead to a substantial increase in spectral efficiency over the existing schemes. Moreover, they are also applicable in ill-posed CSI estimation scenarios, where conventional approaches fail due to a large delay spread. The Bayesian Cramér-Rao bounds are derived to analytically benchmark the estimation performance followed by simulation results that show the improved performance of the proposed techniques.