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\begin{document}

\title{Signal demodulator based on in-phase and quadrature interference-robust
feature}



\author[1]{Wen Deng}%
\author[2]{XIN CAI}%
\author[1]{Xiang Wang}%
\author[1]{Zhitao Huang}%
\affil[1]{National University of Defense Technology}%
\affil[2]{Academy of Military Science of the People's Liberation Army}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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