Underwater Acoustic Communication Channel Modeling using Reservoir Computing

TL;DR

This paper demonstrates that reservoir computing, specifically Echo State Networks, can effectively model complex underwater acoustic channels with higher accuracy than traditional deep learning methods, using real-world data from water tanks and lakes.

Contribution

The study introduces a data-driven approach using reservoir computing for accurate UWA channel modeling and explores transfer learning to enhance modeling capabilities.

Findings

ESN outperforms deep neural networks in modeling UWA channels

ESN achieves up to 40% lower error in chaotic environments

Reservoir computing effectively captures the dynamics of underwater channels

Abstract

Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment. In order to improve UWA communications, modeling and understanding the UWA channel is indispensable. However, there exist many challenges due to the high uncertainties of the underwater environment and the lack of real-world measurement data. In this work, the capability of reservoir computing and deep learning has been explored for modeling the UWA communication channel accurately using real underwater data collected from a water tank with disturbance and from Lake Tahoe. We leverage the capability of reservoir computing for modeling dynamical systems and provided a data-driven approach to modeling the UWA channel using Echo State Network (ESN). In addition, the potential application of transfer learning to reservoir computing has been examined. Experimental results show that ESN is able to model chaotic UWA channels with better performance compared to popular deep learning models in terms of mean absolute percentage error (MAPE), specifically, ESN has outperformed deep neural network by 2% and as much as 40% in benign and chaotic UWA respectively.