Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks
This addresses communication reliability in challenging shallow water environments, representing an incremental improvement with a specific gain.
The paper tackled Doppler effect distortion in shallow water acoustic communications by proposing a deep belief network-based demodulation method, achieving a 2dB error margin in bit error rate despite instantaneous frequencies.
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).