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Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 – December 1, 2019, Proceedings, Part I

Research Article

Deep Learning Based Single-Channel Blind Separation of Co-frequency Modulated Signals

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  • @INPROCEEDINGS{10.1007/978-3-030-41114-5_45,
        author={Chen Chen and Zhufei Lu and Zhe Guo and Feng Yang and Lianghui Ding},
        title={Deep Learning Based Single-Channel Blind Separation of Co-frequency Modulated Signals},
        proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part I},
        proceedings_a={CHINACOM},
        year={2020},
        month={2},
        keywords={Single-channel blind separation (SCBS) Deep learning (DL) Bidirectional recurrent neural network (BRNN)},
        doi={10.1007/978-3-030-41114-5_45}
    }
    
  • Chen Chen
    Zhufei Lu
    Zhe Guo
    Feng Yang
    Lianghui Ding
    Year: 2020
    Deep Learning Based Single-Channel Blind Separation of Co-frequency Modulated Signals
    CHINACOM
    Springer
    DOI: 10.1007/978-3-030-41114-5_45
Chen Chen1, Zhufei Lu2, Zhe Guo3, Feng Yang1,*, Lianghui Ding1
  • 1: Department of Electronic Engineering
  • 2: Yichang Testing Institute of Technology Research
  • 3: Shanghai Microwave Research Institute and CETC Key Laboratory of Data Link Technology
*Contact email: yangfeng@sjtu.edu.cn

Abstract

This paper presents our results in deep learning (DL) based single-channel blind separation (SCBS). Here, we propose a bidirectional recurrent neural network (BRNN) based separation method which can recover information bits directly from co-frequency modulated signals after end-to-end learning. Aiming at the real-time processing, a strategy of block processing is proposed, solving high error rate at the beginning and end of each block of data. Compared with the conventional PSP method, the proposed DL separation method achieves better BER performance in linear case and nonlinear distortion case with lower computational complexity. Simulation results further demonstrate the generalization ability and robustness of the proposed approach in terms of mismatching amplitude ratios.

Keywords
Single-channel blind separation (SCBS) Deep learning (DL) Bidirectional recurrent neural network (BRNN)
Published
2020-02-27
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-41114-5_45
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