SBL-Based Joint Channel Estimation and ML Sequence Detection in STTC MIMO-OFDM Systems

This paper presents sparse Bayesian learning (SBL)-based estimation schemes for an approximately sparse wireless multipath channel impulse response (MCIR) in a space-time trellis coded (STTC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. The proposed schemes consider space-time trellis encoding over consecutive OFDM symbols and employ the multiple response extension of SBL (MSBL) framework to design a pilot-based channel estimation scheme. Subsequently, the trellis-based Viterbi decoder is systematically incorporated into the expectation maximization (EM) framework to propose a novel joint channel estimation and maximum likelihood sequence detection (MLSD) paradigm, the solution of which is shown to lead to an MSBL-based MIMO channel estimate in the E- step followed by a novel modified path-metric- based Viterbi decoder in the M-step with a reduced decoding complexity. Simulation results are presented to illustrate the superior performance of the proposed MSBL-based schemes over some of the existing non-sparse and sparse channel estimation techniques.