  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).
