Autoencoder-based Channel Denoiser for Block Codes 


Vol. 49,  No. 11, pp. 1607-1613, Nov.  2024
10.7840/kics.2024.49.11.1607


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  Abstract

This paper proposes a new neural network-based channel denoiser designed to remove wireless channel noise before data decoding, thereby improving block code decoding performance. The proposed channel denoiser utilizes an autoencoder structure known to be effective in noise removal within image data. For performance comparison with the existing multi-layer perceptron-based model, the same neural network decoder was applied to Polar codes, LDPC codes, and BCH codes, and the denoiser effect was tested in AWGN and Rayleigh fading channels. Experiments confirmed that the proposed model utilizing an autoencoder-based denoiser significantly outperforms the traditional model using a multi-layer perceptron-based denoiser regarding block error rate across all assumed channel environments and channel codes.

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[IEEE Style]

Y. Cho, N. Kwon, J. Kim, H. Song, "Autoencoder-based Channel Denoiser for Block Codes," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 11, pp. 1607-1613, 2024. DOI: 10.7840/kics.2024.49.11.1607.

[ACM Style]

Yeji Cho, Nahyeon Kwon, Junghyun Kim, and Hong-Yeop Song. 2024. Autoencoder-based Channel Denoiser for Block Codes. The Journal of Korean Institute of Communications and Information Sciences, 49, 11, (2024), 1607-1613. DOI: 10.7840/kics.2024.49.11.1607.

[KICS Style]

Yeji Cho, Nahyeon Kwon, Junghyun Kim, Hong-Yeop Song, "Autoencoder-based Channel Denoiser for Block Codes," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 11, pp. 1607-1613, 11. 2024. (https://doi.org/10.7840/kics.2024.49.11.1607)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880