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Cyclostationarity-Based Narrowband Interference Suppression for Heterogeneous Networks Using Neural Network

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Abstract

This paper proposes a narrowband interference (NBI) suppression algorithm for Direct Sequence-Code Division Multiple Access systems. The NBI is considered from heterogeneous networks, and predicted based on its cyclostationary characteristic using a nonlinear feed-forward neural network predictor which eliminates the nonlinearity of the spread spectrum (SS) signal in the NBI prediction. To further improve the suppression performance, this paper exploits the structure of the spreading code, and proposes an iterative code-aided algorithm to jointly estimate the NBI and the SS signal. Simulation results reveal that the proposed algorithm largely outperforms the conventional linear prediction filtering and linear-conjugate linear polyperiodically time-varying filtering methods in both the signal to interference plus noise ratio improvement and the bit error rates, when it operates in NBI-contaminated environments.

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Authors and Affiliations

  1. State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, People’s Republic of China

    Zan Yang & Yuping Zhao

  2. QUALCOMM, San Diego, CA, USA

    Xiaoxin Zhang

Authors
  1. Zan Yang
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  2. Xiaoxin Zhang
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  3. Yuping Zhao
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Correspondence to Yuping Zhao.

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Yang, Z., Zhang, X. & Zhao, Y. Cyclostationarity-Based Narrowband Interference Suppression for Heterogeneous Networks Using Neural Network. Wireless Pers Commun 68, 993–1012 (2013). https://doi.org/10.1007/s11277-011-0495-0

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  • DOI: https://doi.org/10.1007/s11277-011-0495-0

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