Underwater Acoustic Communication Channel Modeling Using Reservoir Computing

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