Soft-Decision-Driven Sparse Channel Estimation and Turbo Equalization for MIMO Underwater Acoustic Communications

Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori soft-decision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD-based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators, and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes.