Deep Learning for Blind Detection of Interleaver and Scrambler
Weizhi Zhang, Boxiao Shen, Chuan Zhen Huang · 2021
This paper considers the blind detection of interleaver and scrambler from the Gaussian noisy channel. Specifically, several architectures of the detectors are proposed based on the convolutional neural network (CNN) and recurrent neural network (RNN). By fully exploiting the neural network (NN) structures, the detectors can recognize the interleaver and scrambler with little prior information and only need several codewords. Finally, extensive experiments demonstrate that the constructed detectors can generalize new samples which are not in the training set.