Analysis and Improvement of Channel Equalization for Underwater Acoustic Communication Based on Variational Mode Decomposition and CNN-BiLSTM

Abstract

Neural networks, including conventional neural networks (CNNs) and long- and short-term memory (LSTM) networks, have gained considerable attention in recent years due to their remarkable ability to recognize signals, which make them an ideal option for communication applications in aquatic environments. Despite the promising results achieved through the integration of various algorithms and CNN-LSTM networks, the utilization of these networks necessitates a significant amount of data for training which results in increased complexity of recognition systems. Therefore, it is crucial to explore new solutions that can mitigate these issues while also enhancing the overall performance of underwater acoustic communication systems. Therefore, this paper proposes a VCB framework that integrates the variational modal decomposition (VMD) algorithm, CNN and Bidirectional LSTM (BiLSTM) networks. This framework employs the VMD algorithm to extract energy characteristics from signals that are modulated by filter bank multi-carrier (FBMC) technologies. One-dimensional convolution (Conv1D) is then employed to extract the abstract features, which are subsequently fed into LSTM for time correlation analysis, from the energy values at each time step. Experimental results indicate that the neural network framework utilized in this study significantly diminishes data volume and enhances the recognition rate of FBMC signals, resulting in a lower bit error rate (BER).