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

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).

Underwater Acoustic Communication Receiver Using Deep Belief Network

“…To fairly evaluate the performance of the proposed receiver with the two systems mentioned above, the f c of the BPSK signals is set at 2kHz. In a previous investigation seen in [40], we discovered that the feature extraction ability of the DBN has created a characteristic that is invariant to the influences of the Doppler effect. Therefore, we assume that even though the classification DBN was only trained on f c = 2kHz, the performance of the proposed classification DBN will not be significantly degraded by the range of f c used in the testing dataset used.…”