Multi-block Sparse Bayesian learning channel estimation for OFDM underwater acoustic communication based on fractional Fourier transform

Abstract

In OFDM underwater acoustic communication, according to the sparse characteristics of underwater acoustic communication channel in the time domain, sparse Bayesian learning (SBL) is introduced to make the channel estimation. In order to reduce the use of pilots and improve the transmission efficiency, a sparse pilot structure is adopted, and a multi-blocks sparse Bayesian learning (MBSBL) channel estimation algorithm is proposed. Aiming at the problem of insufficient channel estimation of MBSBL algorithm in the condition of few pilots, the fractional Fourier transform is introduced to compensate the Doppler frequency shift, which further improves the channel estimation performance and output signal-to-noise ratio (SNR). Besides, the complexities of Orthogonal Matching Pursuit (OMP), SBL and MBSBL algorithms are summarized briefly. Simulation and sea trial data show that the proposed algorithm can save half the number of pilots and the channel estimation effect of proposed algorithm is better than that of OMP algorithm and SBL algorithm.