OMP-Based Channel Estimation without Prior Information for Underwater Acoustic OFDM Systems

A crucial prerequisite for orthogonal matching pursuit (OMP), a widely-used channel estimation method in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communication systems, is the determination of a termination condition. However, the appropriate condition, which is commonly considered equal to the physical sparsity of the UWA channel, actually dramatically varies with the suffered noise, thus possibly leading to extremely unstable estimation performance. Existing OMP-based algorithms attempt to solve this problem by elaborately adjusting iteration numbers to balance the proportion of genuine channel taps and noise in the reconstructed signal based on noise levels, which inevitably increases the dependency on the prior information, i.e., signal-to-noise ratio (SNR). In order to overcome this challenge, an intuitive idea is eliminating the influence of noise to restore the originally sparse signal before implementing the standard OMP, naturally avoiding the variation of termination conditions. Considering the powerful ability of deep learning, we imitate and elegantly modify the feed-forward denoising convolution neural network (DnCNN), one of the most typical neural networks for image denoising, to develop our prior-information-free denoising OMP (DnOMP) algorithm with a constant iteration number. Simulation results validate that, compared to the standard OMP with the dynamic termination condition, the DnOMP can reduce the normalized mean square error (NMSE) by 39.47%.