An improved sparsity‐aware normalized least‐mean‐square scheme for underwater communication

Abstract

Underwater communication (UWC) is widely used in coastal surveillance and early warning systems. Precise channel estimation is vital for efficient and reliable UWC. The sparse direct‐adaptive filtering algorithms have become popular in UWC. Herein, we present an improved adaptive convex‐combination method for the identification of sparse structures using a reweighted normalized least‐mean‐square (RNLMS) algorithm. Moreover, to make RNLMS algorithm independent of the reweighted l1‐norm parameter, a modified sparsity‐aware adaptive zero‐attracting RNLMS (AZA‐RNLMS) algorithm is introduced to ensure accurate modeling. In addition, we present a quantitative analysis of this algorithm to evaluate the convergence speed and accuracy. Furthermore, we derive an excess mean‐square‐error expression that proves that the AZA‐RNLMS algorithm performs better for the harsh underwater channel. The measured data from the experimental channel of SPACE08 is used for simulation, and results are presented to verify the performance of the proposed algorithm. The simulation results confirm that the proposed algorithm for underwater channel estimation performs better than the earlier schemes.