Blind Identification of Channel Codes under AWGN and Fading Conditions via Deep Learning

Peng, Haifeng; Cao, Chunjie; Sun, Yang; Li, Haoran; Wen, Xiuhua

doi:10.1109/nana56854.2022.00020

Cited by 3 publications

(1 citation statement)

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“…In [10], a FEC classification framework based on hidden Markov models was proposed, which modeled and analyzed the structural differences of FEC using transition probability and emission probability. In order to align with practical scenarios, reference [11] and [12] considered multipath and fading channels. Although the above methods apply the latest technology, they are limited to closed-set recognition, which does not align with fully blind scenarios.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Hu,

Yang

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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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.

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Section: Introductionmentioning

confidence: 99%