SBL-Based Joint Sparse Channel Estimation and Maximum Likelihood Symbol Detection in OSTBC MIMO-OFDM Systems

This paper presents sparse Bayesian learning (SBL)-based schemes for approximately sparse channel estimation in an orthogonal space-time block coded (OSTBC) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) wireless system. The parameterized prior-based SBL framework is employed to present a pilot scheme for an ill-posed OSTBC MIMO-OFDM channel estimation scenario. Maximum likelihood symbol detection (MLSD) has been incorporated in the expectation-maximization framework for SBL-based channel estimation. This has led to the development of a novel scheme for joint approximately sparse channel estimation and symbol detection. The proposed scheme performs SBL-based channel estimation in the E-step followed by a modified ML decision metric-based symbol detection in the M-step. Bayesian Cramér-Rao bounds are obtained for the genie minimum mean-squared error estimators corresponding to the SBL schemes. Closed-form bit error probability expressions are derived for the MLSD in the presence of SBL-based channel estimation errors. Simulation results are presented towards the end to validate the theoretical bounds and illustrate the performance of the proposed techniques.