Sparse Kalman Filtering (SKF) Aided Doubly-Selective Channel Estimation in STTC MIMO-OFDM Systems

This work develops a novel scheme for doubly-selective sparse channel estimation in a space-time trellis coded (STTC) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless systems. Toward this end, a pilot based doubly-selective sparse channel estimation model is developed, wherein the time-evolution of the wireless channel is modeled using a first-order autoregressive (AR1) process. This is followed by the proposed online pilot-based sparse Kalman filter (P-SKF) for channel estimation. The proposed P-SKF scheme combines the advantages of the Kalman filter (KF) and sparse Bayesian learning (SBL) techniques to exploit the temporal correlation as well as the sparse multipath delay profile of the wireless channel. In addition, the proposed PSKF technique also exploits the simultaneous-sparsity across all the transmit-receive antenna pairs for improved channel estimation. The recursive Bayesian Cramér-Rao lower bound (BCRLB) is also derived to benchmark the mean square error (MSE) performance of the proposed technique. Simulation results are presented to evaluate the MSE and frame error rate (FER) performance of the proposed technique and compare with the existing schemes.