Deep Learning Based High-Speed Underwater Acoustic Communication System Design

Abstract

Underwater acoustic (UA) communication is vital for applications like oceanographic data collection and defence but faces challenges like limited bandwidth and multipath interference. This research enhances the conventional OFDM method with a neural network (NN)-based approach, leveraging CNNs, LSTMs, and MLPs for improved channel estimation and adaptability. Experiments in simulations, tank tests, and river trials demonstrated the superiority of the NN-based receiver, particularly under high non-linearity and Doppler effects, advancing UA communication reliability and adaptability.