Sparse Bayesian Learning for Channel Estimation in Time-Varying Underwater Acoustic OFDM Communication

In this paper, we study the sparse Bayesian learning (SBL) framework for channel estimation in underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication systems, which provides a desirable property of preventing structural error with fewer convergence errors for sparse signal reconstruction compared with the compress sensing (CS)-based methods. First, we design a SBL-based channel estimator for block-by-block processing using the channel sparse structure independently in each block. Then, we propose a joint channel model after Doppler compensation for multi-block joint processing, where the delays of the channels for several consecutive blocks are similar and the path gains exhibit temporal correlation, and we denote a temporal correlation coefficient for path gains to evaluate the strength of the correlation. Furthermore, we propose the temporal multiple SBL (TMSBL)-based channel estimator to jointly estimate the channels by taking advantage of the channel coherence between consecutive OFDM blocks. Results of numerical simulation and sea trial demonstrate the effectiveness of the SBL and TMSBL channel estimator algorithms in time-varying UWA channel, which achieve better channel estimation performance and lower bit error rate compared with the existing CS-based methods, such as orthogonal matching pursuit (OMP) and simultaneous OMP, especially the TMSBL estimator achieves the best performance in strong temporal correlated channels and maintains robustness in weak temporal correlated channels.