Blind Classification of Error-Correcting Codes for Enhancing Spectral Efficiency of Wireless Networks

Kwon, Soonhee; Shin, Dong-Joon

doi:10.1109/tbc.2021.3061984

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…To ensure the credibility of the experiment, all encoding parameters utilized in this paper were extracted from reference [4][5][6][7]10,13]. We employ accuracy to assess the performance of algorithm, defining one correct event as the accurate classification and accurate parameters estimation.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For related columns, although the hamming weight is not zero, its value is still smaller than that of independent columns. So, it can be distinguished by a certain threshold [6] . The threshold calculation method will be introduced in the next section.…”

Section: For Block Codes With Parameters ( )mentioning

confidence: 99%

“…The experiment compares the performance of the proposed algorithm with the reference [5] and [6] algorithms under different SNR. The accuracy results are shown in Figure 6.…”

Section: Performance Comparison Of Different Algorithmsmentioning

confidence: 99%

“…In [5], the authors further extended the rankbased classification method to noisy scenarios, using the Gauss-Jordan elimination through pivoting (GJETP) to process codeword matrices and then deriving the optimal threshold for rank calculation based on the law of large numbers. In [6], a new method for rank calculation was proposed, which achieved a higher accuracy by repeating random row arrangements on the codeword matrix. However, it had high computational complexity and required a large amount of data.…”

Section: Introductionmentioning

confidence: 99%

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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: Simulation Resultsmentioning

confidence: 99%

Section: For Block Codes With Parameters ( )mentioning

confidence: 99%

“…The experiment compares the performance of the proposed algorithm with the reference [5] and [6] algorithms under different SNR. The accuracy results are shown in Figure 6.…”

Section: Performance Comparison Of Different Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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)

View full text Add to dashboard Cite

show abstract

“…In order to improve the accuracy of rank criterion, a random row permutation of the intercepted matrix is used, but a theoretical analysis of the accuracy of rank criterion according to the number of random row permutations was not yet performed. In [18], a new algorithm for calculating the rank of intercepted matrix is proposed. This algorithm does not need existing parameters and basically realizes blind recognition, but it does not fundamentally get rid of the problem of poor anti-jamming ability of the matrix rank algorithm.…”

Section: Introductionmentioning

confidence: 99%

Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm

2021

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Forward error correction codes (FEC) are one of the vital sections of modern communication systems; therefore, recognition of the coding type is an important issue in non-cooperative communication. At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes. However, based on information asymmetry, the receiver cannot know the types of channel coding previously used in non-cooperative systems such as cognitive radio and remote sensing of communication. Therefore, it is important to recognize the error-correcting encoding type with no prior information. Although the traditional algorithm can also recognize the type of codes, it is only applicable to the case without errors, and its practicability is poor. In the paper, we propose a new method to identify the types of FEC codes based on depth distribution in non-cooperative communication. The proposed algorithm can effectively recognize linear block codes, convolutional codes, and Turbo codes under a low error probability level, and has a higher robustness to noise transmission environment. In addition, an improved matrix estimation algorithm based on Gaussian elimination was adopted in this paper, which effectively improves the parameter identification in a noisy environment. Finally, we used a general framework to unify all the reconstruction algorithms to simplify the complexity of the algorithm. The simulation results show that, compared with the traditional algorithm based on matrix rank, the proposed algorithm has a better anti-interference performance. The method proposed is simple and convenient for engineering and practical applications.

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Blind recognition of channel codes based on a multiscale dilated convolution neural network

Liu,

Cao,

2024

Physical Communication

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Blind Classification of Error-Correcting Codes for Enhancing Spectral Efficiency of Wireless Networks

Cited by 5 publications

References 16 publications

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

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

Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm

Blind recognition of channel codes based on a multiscale dilated convolution neural network

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