Distinction of self-synchronous scrambled linear block codes based on multi-fractal spectrum

Li, Xinhao; Zhang, Min; Han, Shunan; Yuan, Quan

doi:10.21629/jsee.2016.05.04

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Scrambler type recognition based on shallow neural networks

Tan,

Zhong,

Jiang

2023

2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)

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Scrambler type recognition based on shallow neural networks

Tan,

Zhong,

Jiang

2023

2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP)

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Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features

Tan¹,

Zhang²,

Zhong³

2022

Applied Sciences

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Aiming to solve the problem of the distinction of scrambled linear block codes, a method for identifying the scrambling types of linear block codes by combining correlation features and convolution long short-term memory neural networks is proposed in this paper. First, the cross-correlation characteristics of the scrambling sequence symbols are deduced, the partial autocorrelation function is constructed, the superiority of the partial autocorrelation function is determined by derivation, and the two are combined as the input correlation characteristics. A shallow network combining a convolutional neural network and LSTM is constructed; finally, the linear block code scrambled dataset is input into the network model, and the training and recognition test of the network is completed. The simulation results show that, compared with the traditional algorithm based on a multi-fractal spectrum, the proposed method can identify a synchronous scrambler, and the recognition accuracy is higher under a high bit error rate. Moreover, the method is suitable for classification under noise. The proposed method lays a foundation for future improvements in scrambler parameter identification.

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Distinction of self-synchronous scrambled linear block codes based on multi-fractal spectrum

Cited by 2 publications

References 0 publications

Scrambler type recognition based on shallow neural networks

Scrambler type recognition based on shallow neural networks

Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features

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