Blind classification and parameters estimation of forward error correction codes based on data cleaning

Yuanqing Wang; Pengjiang Hu; Junan Yang; Xin Wang; Chuanrong Jiang

doi:10.1117/12.3031309

19 July 2024 Blind classification and parameters estimation of forward error correction codes based on data cleaning

Yuanqing Wang, Pengjiang Hu, Junan Yang, Xin Wang, Chuanrong Jiang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131815D (2024) https://doi.org/10.1117/12.3031309
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

The classification and parameters estimation of forward error correction (FEC) codes are significant in non-cooperative communication. Previous solutions suffer from poor noise resistance or only applicable to closed-set recognition. To address these issues, this paper proposes a FEC classification and parameters estimation algorithm based on data cleaning. It utilizes the reliability criterion of soft information as cleaning rules to reduce erroneous codewords, thereby improving the accuracy of rank calculation. A novel classification criterion is proposed, which utilizes all rank information and further improves classification accuracy. Simulation results show the effectiveness of both the data cleaning mechanism and novel classification criteria in enhancing recognition accuracy. Compared with existing algorithms, the proposed algorithm has a higher accuracy and an improvement of approximately 1dB in noise resistance performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuanqing Wang, Pengjiang Hu, Junan Yang, Xin Wang, and Chuanrong Jiang "Blind classification and parameters estimation of forward error correction codes based on data cleaning", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131815D (19 July 2024); https://doi.org/10.1117/12.3031309

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