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Impulse Noise Suppression by Deep Learning-Based Receivers in OFDM Systems

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Abstract

Orthogonal Frequency Division Multiplexing (OFDM) systems are prone to signal corruption caused by strong and frequent impulses, which can be further exacerbated by multipath fading. Recent evolutions highlight the efficacy of a deep neural network (DNN) receiver in intrinsically estimating channel state information and recovering data explicitly, even without presuming the signal-to-noise ratio (SNR) level. However, the conventional DNN-based receiver, trained on data generated from computer simulations with WINNER II channel model and additive white Gaussian noise (AWGN), is susceptible to substantial performance degradation when subject to impulse noise. To address this challenge, this paper proposes fine-tuning the DNN model using impulse noise-laced data samples during subsequent training. The proposed method aims to enhance representation learning and improve the robustness of the receiver against impulse noise. The efficacy of the DNN-based receiver is assessed by comparing its bit error rate (BER) performance to that of a compressive sensing-based receiver, enabled by the consensus alternating direction method of multipliers (ADMM). Remarkably, the proposed DNN-based receiver achieves BER performance comparable to the clipping-featured receiver, which requires knowledge of the SNR value, an assumption relaxed by our enhanced DNN approach. Furthermore, extensive simulations demonstrate the promising robustness of the deep learning-based approach against impulse noise model mismatches between training and testing scenarios.

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Data are available upon request.

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Acknowledgements

The authors acknowledged the research funding received from the Ministry of Science and Technology (MOST) of Taiwan for the major research projects.

Funding

This work was supported by the Ministry of Science and Technology (MOST) of Taiwan under grant no. MOST 109-2221-E-011-119-, MOST 110-2221-E-011-066- and MOST 111-2221-E-011-060 -.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Der-Feng Tseng & Che-Shien Lin

  2. Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan

    Shu-Ming Tseng

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  1. Der-Feng Tseng
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  2. Che-Shien Lin
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Contributions

D-FT, the corresponding author, contributed to the study conception, performed system design and experiments, and wrote the first draft of the manuscript. C-SL conducted computer simulations. S-MT made significant contributions to the analysis and conception of the study. All authors read and approved the final manuscript.

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Correspondence to Der-Feng Tseng.

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Tseng, DF., Lin, CS. & Tseng, SM. Impulse Noise Suppression by Deep Learning-Based Receivers in OFDM Systems. Wireless Pers Commun 134, 557–580 (2024). https://doi.org/10.1007/s11277-024-10919-5

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