Journal
2019 IEEE VTS ASIA PACIFIC WIRELESS COMMUNICATIONS SYMPOSIUM (APWCS 2019)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/VTS-APWCS.2019.8851669
Keywords
Demodulation; Signal Processing; Neural Network; Feature Extraction; Machine Learning
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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).
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