Sparsity-Aware Adaptive Turbo Equalization for Underwater Acoustic Communications in the Mariana Trench
Junyi Xi, Shefeng Yan, Lijun Xu, Chaohuan Hou
Abstract
Reliable acoustic communication between submersibles and surface vessels plays a critical role in deep-sea exploration. Adaptive turbo equalization can effectively combat the selective fading of underwater acoustic channels, thereby becoming one of the enabling technologies for single-carrier deep-sea vertical acoustic communications. Existing adaptive turbo equalizer designs are usually based on a minimum-mean-squared-error criterion or a minimum-mean-absolute-error criterion. These criteria are inherently suboptimal with respect to the achievable symbol error rate (SER). In this article, an improved proportionate normalized minimum-SER (IPNMSER) algorithm is proposed for adaptive turbo equalization in deep-sea vertical acoustic communications. The proposed algorithm utilizes the minimum-SER (MSER) criterion to derive the equalizer update equations, aiming to minimize the system's SER directly. In addition, because the deep-sea vertical channel has a sparse structure, which leads to a sparse equalizer, a sparsity-aware proportionate-type approach is therefore incorporated into the framework of the MSER criterion to achieve faster convergence. To investigate the effectiveness of the proposed algorithm, we conducted a deep-sea vertical acoustic-communication experiment in the Challenger Deep of the Mariana Trench. The results demonstrated that the proposed IPNMSER algorithm can outperform a conventional normalized MSER algorithm and other well-known proportionate-type algorithms, achieving error-free detection for all data blocks over a vertical communication range of approximately 10500 m.