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A Single-Channel Blind Separation Convolutional Network Combined with Attention Mechanism for Communication Signals

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Abstract

With the rapid development of information transfer technology and the influence of complex electromagnetic environment, the signal components in communication systems are becoming more and more complex, and the spectrum congestion is further aggravated, which seriously affects the performance of communication systems. Single-channel blind source separation technology is an effective solution, but the existing algorithms are deficient in terms of estimation accuracy, time delay and computational complexity. Based on the powerful feature extraction capability and timing signal processing capability of deep learning, this paper proposes an end-to-end time-domain blind separation convolutional network structure, including three parts: encoder, separation module and decoder. The attention mechanism module is also introduced into the encoding layer of the network to enhance the deep feature extraction ability of the network, highlight the influence of important timing features of communication signals, further improve the separation performance and enhance the practicality of the network. The experimental results show that the algorithm has superior performance and can effectively separate the mixed communication signal of various modulation types, and it can also extract own-side communication signals from high-power masked signals. Compared with the existing algorithms, the network in this paper has high separation accuracy and low computational complexity, which breaks the bottleneck of traditional single-channel blind separation algorithms in complex communication electromagnetic environment.

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Data Availability

The datasets generated or analyzed and material during this current study are available from the corresponding author on reasonable request.

Code Availability

The codes during this current study are available from the corresponding author on reasonable request.

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Acknowledgements

We gratefully acknowledge the anonymous reviewers who read the drafts and provided many helpful suggestions.

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Authors and Affiliations

  1. School of Telecommunications Engineering, Xi’dian University, Xi’an, 710071, Shaanxi, China

    Weihong Fu, Wensheng Zhao & Xinyu Zhang

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  1. Weihong Fu
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  2. Wensheng Zhao
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Correspondence to Weihong Fu.

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Fu, W., Zhao, W. & Zhang, X. A Single-Channel Blind Separation Convolutional Network Combined with Attention Mechanism for Communication Signals. Circuits Syst Signal Process 43, 1240–1269 (2024). https://doi.org/10.1007/s00034-023-02518-9

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