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

Abstract

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.

Special Issue on Advance in Digital Signal, Image and Video Processing

“…Moreover, it shows that the relationship between performance, quality and acceptance is not fully recognized. A method for identifying the scrambling types of linear block codes (LBC) by combining correlation features and convolution long short-term memory (LSTM) neural networks (NNs) is proposed in [1]. In order to improve the bit error performance while maintaining low decoding steps, the authors of [2] introduce a neural network subcode that can achieve optimal decoding performance and combine it with the traditional fast successive-cancellation (SC) decoding algorithm.…”