Underwater Acoustic Communication Channel Modeling using Deep Learning

Abstract

With the recent increase in the number of underwater activities, having effective underwater communication systems has become increasingly important. Underwater acoustic communication has been widely used but greatly impaired due to the complicated nature of the underwater environment. In a bid to better understand the underwater acoustic channel so as to help in the design and improvement of underwater communication systems, attempts have been made to model the underwater acoustic channel using mathematical equations and approximations under some assumptions. In this paper, we explore the capability of machine learning and deep learning methods to learn and accurately model the underwater acoustic channel using real underwater data collected from a water tank with disturbance and from lake Tahoe. Specifically, Deep Neural Network (DNN) and Long Short Term Memory (LSTM) are applied to model the underwater acoustic channel. Experimental results show that these models are able to model the underwater acoustic communication channel well and that deep learning models, especially LSTM are better models in terms of mean absolute percentage error.

An improved underwater wireless sensor network communication using Internet of Things and signal to noise ratio analysis

“…The deep learning (DL)-based UAC channel model was presented in Onasami et al 25 This method uses real-time data for the processing, which was gathered from Lake Tahoe. Both machine learning and deep learning (ie, deep neural network [DNN] and long short-term memory [LSTM]) approaches were used for accurately estimating the UWA channel.…”