Blind identification of channel coding types based on deep neural networks

被引：0

作者：

Yang Z. ^{[1
,2
]}

Zhang T. ^{[1
,2
]}

Ma K. ^{[1
,2
]}

Zou H. ^{[1
,2
]}

机构：

[1] School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing

[2] Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2024年 / 46卷 / 05期

关键词：

blind identification; channel code recognizer; deep neural networks; word length;

D O I：

10.12305/j.issn.1001-506X.2024.05.35

中图分类号：

学科分类号：

摘要：

In order to solve the problem that the current recognition algorithm can only recognize one or two code types and the complexity of manually extracting features, two channel coding type recognizers based on the deep neural network model are proposed, namely, convolutional neural network (CNN) recognizer and recursive CNN (RCNN) recognizer, used to identify different types of channel codewords in received data. The soft demodulation sequence to be recognized is treated as the sentence vector of text classification in natural language processing, input into the pre trained deep neural network recognizer for recognition, and analyze the influence of word length on recognition accuracy, and obtain the most appropriate word length. The experimental results show that both types of recognizers can effectively recognize various types of channel codes in the received data, and the recognition accuracy of the CNN recognizer can reach over 99% when the signal-to-noise ratio is 3 dB, while the RCNN recognizer can achieve over 99% recognition accuracy at 1 dB. © 2024 Chinese Institute of Electronics. All rights reserved.

引用

页码：1820 / 1829

页数：9

共 22 条

[1] YAN H., Research on blind identification technology of channel coding[D], (2018)
[2] XIE H, HUANG Z T, WANG F H., Research progress of channel coding blind identification technology, Journal of Electronics, 41, 6, pp. 1166-1176, (2013)
[3] YANG S, TANG Y., Text classification based on convolutional neural network and attention model, Proc. of the International Conference on Artificial Intelligence and Big Data, pp. 67-73, (2020)
[4] XIA T, WU H C., Novel blind identification of LDPC codes using average LLR of syndrome a posteriori probability, IEEE Trans, on Signal Processing, 62, 3, pp. 632-640, (2014)
[5] YU P D, PENG H, GONG K X, Et al., LDPC code open set recognition based on searching for low-weight codeword algorithm, Journal of Communications, 38, 6, pp. 108-117, (2017)
[6] WU G, ZHANG B N, WEN X, Et al., Blind recognition of BCH code based on Galois field Fourier transform, Proc. of the International Conference on Wireless Communications (^ Signal Processing, (2015)
[7] WANG Y, WANG C, WANG X, Et al., A parameter recognition algorithm for non censored polarization codes based on soft decision, Systems Engineering and Electronic Technology, 45, 10, pp. 3291-3299, (2023)
[8] LIU J X, ZHANG T Q, BA1 H J, Et al., Blind identification algorithm for polarization code parameters based on information matrix estimation, Systems Engineering and Electronic Technology, 44, 2, pp. 668-676, (2022)
[9] SWAMINATHAN R, MADHUKUMAR A S., Classification of error correcting codes and estimation of interleaver parameters in a noisy transmission environment, IEEE Trans, on Broadcasting, 63, 3, pp. 463-478, (2017)
[10] TAO Z Y, YAN M H, LIU Y., Channel coding closed set recognition based on temporal convolutional network, Journal of Huazhong University of Science and Technology (Natural Science Edition), 50, 3, pp. 12-17, (2022)

← 1 2 3 →