Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks

TL;DR

This paper introduces machine learning-based demodulation techniques using deep belief networks for shallow water acoustic communications, effectively mitigating Doppler effects and maintaining low error rates.

Contribution

It presents novel ML-based demodulation methods combining feature extraction and classification, demonstrating Doppler invariance in challenging shallow water environments.

Findings

Achieves approximately 2dB error margin in BER

Demonstrates robustness against Doppler-induced frequency distortions

Validates effectiveness of ML-based demodulation in SWAC

Abstract

Shallow water environments create a challenging channel for communications. In this paper, we focus on the challenges posed by the frequency-selective signal distortion called the Doppler effect. We explore the design and performance of machine learning (ML) based demodulation methods --- (1) Deep Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow Water Acoustic Communication (SWAC). The proposed method comprises of a ML based feature extraction method and classification technique. First, the feature extraction converts the received signals to feature images. Next, the classification model correlates the images to a corresponding binary representative. An analysis of the ML based proposed demodulation shows that despite the presence of instantaneous frequencies, the performance of the algorithm shows an invariance with a small 2dB error margin in terms of bit error rate (BER).