Joint channel estimation and detection using Markov chain Monte Carlo method over sparse underwater acoustic channels

This study proposes a novel approach to joint channel estimation and detection of orthogonal frequency division multiplexing transmission over underwater acoustic (UWA) multipath channels exhibiting cluster sparsity. Unlike most sparse channel estimations, the authors exploit the cluster-sparsity characteristic of UWA channels without additional prior information. They adopt a modified spike-and-slab prior model in their non-parametric Bayesian learning framework. To avoid the need for a closed-form Bayesian estimate, they apply the Markov chain Monte Carlo technique to joint achieve channel estimation and signal detection. The proposed solution is amenable to being integrated with soft-input soft-output decoding to improve the performance through turbo iteration. Simulation results demonstrate improved bit error rate of the proposed algorithm over existing algorithms.