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Signal demodulator based on in-phase and quadrature interference-robust feature
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  • Wen Deng,
  • XIN CAI,
  • Xiang Wang,
  • Zhitao Huang
Wen Deng
National University of Defense Technology

Corresponding Author:[email protected]

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Academy of Military Science of the People's Liberation Army
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Xiang Wang
National University of Defense Technology
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Zhitao Huang
National University of Defense Technology
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In this letter, the issue of mitigating strong co-channel interference (CCI) in communication systems is addressed. Unlike conventional model-based methods, a novel data-driven scheme is proposed. A recurrent neural network (RNN) is trained to directly demodulate the desired signal under strong CCI. Instead of inputting the original received signal, in-phase and quadrature interference-robust features (IRF) are extracted through preprocess. The RNN is then trained offline to implement sequence labelling, with the IRF sequences and known code sequences of the desired signal as inputs and ground-truth labels. Meanwhile, a guard zone is introduced when loading the IRF sequences to enable better contextual information exploitation by the RNN demodulator. Online tests validated the low bit error rate (BER) of the RNN demodulator, under strong CCI. Moreover, the proposed scheme outperformed existing model-based and data-driven interference mitigation schemes in terms of the BER, especially in low signal-to-interference ratio region. Inspiringly, the proposed data-driven scheme generalized well to varied unseen test conditions.
22 Oct 2022Submitted to Electronics Letters
24 Oct 2022Submission Checks Completed
24 Oct 2022Assigned to Editor
27 Oct 2022Reviewer(s) Assigned
31 Oct 2022Review(s) Completed, Editorial Evaluation Pending
08 Nov 2022Editorial Decision: Revise Minor
12 Nov 20221st Revision Received
13 Nov 2022Review(s) Completed, Editorial Evaluation Pending
13 Nov 2022Submission Checks Completed
13 Nov 2022Assigned to Editor
21 Nov 2022Editorial Decision: Accept
Jan 2023Published in Electronics Letters volume 59 issue 1. 10.1049/ell2.12686