Adaptive algorithm for compressed sensing channel estimation in underwater acoustic OFDM systems

Abstract

This study addresses channel estimation in low frequency, shallow water environments, particularly when the channel exhibits cluster sparsity. To tackle this, we propose the cluster adaptive step-size stagewise orthogonal matching pursuit (CAS-StOMP) algorithm, which selects consecutive atoms correlated with the residuals using the Dice criterion to capture the channel cluster sparsity. Herein, the step size is adjusted based on the difference in energy between channel estimation results. When the energy difference is large, the step size increases to accelerate convergence; otherwise, a smaller step size is adopted to improve the estimation accuracy. This approach enhances the accuracy and convergence speed. Simulation results show that the proposed algorithm achieves a lower bit error rate (BER) and higher output signal-to-noise ratio (SNR) in low frequency, shallow water acoustic communication as compared with other algorithms that consider only general sparsity.