Neural network error corrector for binary messages on hydro-acoustic channels

Machado, R.Z.; Tenorio, M.F.; Silva, J.R.M.

doi:10.1109/48.180306

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The performance of neural networks in underwater acoustic communication has been highlighted in several works. Machado et al demonstrated the application of a neural network classifier for the detection of underwater acoustic signals, based on Hamming code transmission [10]. Liu et al proposed an integrated learning model consisting of a one-dimensional CNN and a long short-term memory (LSTM) network for orthogonal frequency division multiple access systems in underwater acoustic channels [11].…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Chen

Xing

2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

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

Jiang

Chen

Xing

2023

Preprint

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The application of artificial intelligence in Unmanned Underwater Vehicle communication systems

Jiang,

Xing

2024

Computers and Electrical Engineering

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

Jiang,

Xing

2023

Int. J. Patt. Recogn. Artif. Intell.

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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).

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Neural network error corrector for binary messages on hydro-acoustic channels

Cited by 3 publications

References 9 publications

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

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

The application of artificial intelligence in Unmanned Underwater Vehicle communication systems

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

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