An Adaptive MP Algorithm for Underwater Acoustic Channel Estimation Based on Compressed Sensing

Abstract

In underwater acoustic (UWA) communication systems, inter-carrier interference (ICI) caused by the Doppler effect has significant negative impacts on system performance. To address this issue, this paper introduces a delay-Doppler spread function (DDSF) to account for the effect of ICI and proposes a new compressed sensing (CS) algorithm to estimate channels. Typically, proper termination of the iterative process is a major challenge when applying the orthogonal matching pursuit (OMP) algorithms in channel estimation, while other CS algorithms in the paper have high requirements in terms of complexity and system power consumption. To overcome these limitations, a sparse channel estimation algorithm with an adaptive sparse decision threshold is proposed. Given certain signal-to-noise ratio (SNR) conditions, the proposed algorithm achieves comparable estimation accuracy to OMP with much lower computational cost. Simulation results demonstrate that the proposed algorithm can achieve similar estimation accuracy to OMP at lower computational cost with high SNRs. In conclusion, this paper presents a novel approach to address ICI in UWA communication systems and offers a more efficient algorithm for channel estimation. The results are significant for improving the performance of underwater communication systems and have potential applications in various underwater communication scenarios.

A Disturbance Localization Method for Power System Based on Group Sparse Representation and Entropy Weight Method

“…In the domain of IST algorithms, advancements in reconstruction algorithms primarily stem from developments based on Orthogonal Matching Pursuit (OMP) [21]. This paper adopts the Generalized Orthogonal Matching Pursuit (GOMP) reconstruction algorithm to solve the group sparse model [22].…”