Robust equalization for single-carrier underwater acoustic communication in sparse impulsive interference environment

Impulse noise significantly distorts the background noise within communication systems, causing a notable departure from the Gaussian distribution. This distortion, in turn, exerts a detrimental influence on the performance of conventional single-carrier frequency domain equalization techniques. In this paper, we introduce a novel algorithm for joint channel, interference, and symbol estimation within single-carrier systems. This algorithm is grounded in the framework of variational Bayesian inference and possesses the unique capability of simultaneously accounting for the sparsity inherent in the channel and impulse interference. In the course of processing, it leverages multi-parameter probability distributions to effectively model the intricate posteriori probabilities associated with impulse interference vectors and channel vectors. This, in turn, facilitates the computation of maximum a posteriori estimates and subsequently enables the estimation and elimination of interference. We validate the efficacy of our proposed method through both numerical simulations and the processing of experimental data. Our results unequivocally demonstrate the robustness and manageable computational complexity of our approach within the context of impulse interference-prone environments.