Variational Mode Decomposition for Deep Convolutional Neural Networks in Underwater Acoustic Communication

Abstract

Recently, the application of deep convolutional neural networks (DCNN) in underwater acoustic communication has become a growing research hotspot. However, it is hindered by the large amounts of training data required to achieve a complex communication system. In this paper, we propose a novel approach of combining variational mode decomposition (VMD) and DCNN networks to address this issue. Considering filter-bank multicarrier (FBMC) technologies offer promising performance in terms of spectral efficiency and robustness to interference, and the Bellhop model is also capable of providing effective and accurate communication data, this study adopts FBMC technologies for signal modulation and Bellhop models for communication data. The results show that the VMD-DCNN equalizer has a lower BER compared with the other seven contrast methods, thus further promoting the application and development of VMD-DCNN technologies in underwater acoustic communications.