MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

Abstract

In this paper, we propose a computationally efficient multipath matching pursuit (MMP) channel estimation algorithm for underwater acoustic (UWA) transform domain communication systems (TDCSs). The algorithm, referred to as the MMP-DCD-CV algorithm, is based on the dichotomous coordinate descent (DCD) iterations and cross validation (CV). The MMP-DCD-CV sparse channel estimator in each iteration searches for multiple promising path candidates most relevant to a residual vector and chooses the best candidate. The DCD iterations are used to solve the corresponding least squares problem with low complexity and numerical stability. The CV provides a stopping criterion of the algorithm without a priori information on the channel sparsity and noise level and examines whether the algorithm overfits its data, thus improving the estimation accuracy. The performance of the proposed algorithm is evaluated under simulated sparse UWA channels. The numerical results show that the algorithm achieves better performance than the original MMP algorithm, has lower complexity, and does not require prior knowledge on the channel sparsity and noise level. We also propose an UWA TDCS with sparse channel estimation based on the proposed MMP-DCD-CV algorithm. The proposed UWA communication system is tested by the Waymark simulator, providing the virtual signal transmission in the UWA channel, with a measured Sound Speed Profile and bathymetry. Numerical results demonstrate that the UWA TDCS with the proposed sparse channel estimator offers considerable improvement in system performance compared to other TDCS schemes.