# Underwater Acoustic Communication Channel Modeling using Deep Learning

arXiv: https://arxiv.org/abs/2201.10056 · HTML: https://mlsanity.com/paper/2201.10056/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

## Built on by (most cited first, 3 of 3)

- [Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models](https://mlsanity.com/paper/2205.04886/) (2022)
- [Impact of Learning Rate on Noise Resistant Property of Deep Learning Models](https://mlsanity.com/paper/2205.07856/) (2022)
- [Machine Learning for Underwater Optical Wireless Communication Systems: A Comprehensive Survey](https://mlsanity.com/paper/2609.22294/) (2026)

## Abstract (arXiv)

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.