A Sparse Bayesian Learning Based Joint Channel and Impulsive Noise Estimation Algorithm for Underwater Acoustic OFDM Systems

Abstract

Impulsive noise can significantly affect the performance of underwater acoustic (UA) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, by utilizing the pilot subcarriers, we propose a novel sparse Bayesian learning based expectation maximization algorithm for joint channel estimation and impulsive noise mitigation in UA OFDM systems. Moreover, an adaptive clipping threshold method together with a minimum mean-squared error estimator are developed to improve the estimation of the positions and amplitudes of impulsive noise. The performance of the proposed algorithm is verified both through numerical simulations and by data collected during a UA communication experiment conducted in December 2015 in the estuary of the Swan River, Western Australia. The results show that the proposed algorithm is more effective in mitigating impulsive noise than existing methods.