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.
This paper is supported in part by NSFC China (61771309, 61671301, 61420106008, 61521062), Shanghai Key Laboratory Funding (STCSM18DZ1200102) and CETC Key Laboratory of Data Link Technology Foundation (CLDL-20162306).
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chen, C., Lu, Z., Guo, Z., Yang, F., Ding, L. (2020). Deep Learning Based Single-Channel Blind Separation of Co-frequency Modulated Signals. In: Gao, H., Feng, Z., Yu, J., Wu, J. (eds) Communications and Networking. ChinaCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-41114-5_45
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DOI: https://doi.org/10.1007/978-3-030-41114-5_45
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