基于深层神经网络的信道编码类型盲识别

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

Abstract

Abstract:

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.

Key words: deep neural networks, channel code recognizer, blind identification, word length

CLC Number:

TN911.7

Zongfang YANG, Tianqi ZHANG, Kunran MA, Han ZOU. Blind identification of channel coding types based on deep neural networks[J]. Systems Engineering and Electronics, 2024, 46(5): 1820-1829.

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References 20

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CNN识别器	输出维度
嵌入层	嵌入维度: 128 字长度: 4
卷积层	卷积核高度: 2、3、4、5、6 卷积核宽度: 128 卷积核数量: 256

RCNN识别器	参数
嵌入层	嵌入维度: 128 字长度: 8
LSTM	LSTM尺寸: 128 LSTM层数: 1
池化层	滤波器尺寸: 68 滑动步长: 68

类型	超参数
优化器	Adam
学习率	0.001
批量大小	128
CNN激活函数	ReLU
RCNN激活函数	tanh
dropout率	0.5

信道编码类型	编码参数
LDPC码	256码长, 1/2码率 256码长, 1/3码率512 码长, 1/2码率512 码长, 1/3码率
卷积码	1/2码率, 约束长度为5 1/2码率, 约束长度为7 1/3码率, 约束长度为5 1/3码率, 约束长度为7
极化码	64码长, 1/2码率 128码长, 1/2码率 256码长, 1/2码率 512码长, 1/2码率
Turbo码	1/2码率, (7, 5) 1/2码率, (31, 17) 1/3码率, (7, 5) 1/3码率, (31, 17)

字长度L	CNN		RCNN		读数据/s
字长度L	训练/ms	测试/ms	训练/ms	测试/ms	读数据/s
1	57.34	9.40	201.1	23.76	183
2	29.92	4.69	100.2	10.28	166
3	20.73	3.25	41.79	7.46	105
4	15.00	2.52	33.75	5.36	91
5	11.81	1.89	27.73	4.44	85
6	10.23	1.67	25.39	3.58	80
7	8.93	1.45	19.61	3.06	74
8	7.81	1.24	16.02	2.52	65
9	6.48	1.12	13.75	2.24	22
10	3.28	0.93	12.50	2.04	21

Blind identification of channel coding types based on deep neural networks

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识别器	训练/ms	测试/ms	读数据/s
本文CNN	15.00	2.52	91
本文RCNN	16.02	2.52	65
文献[12]RNN	8.73	1.42	47
文献[12]注意力	21.61	3.52	47
文献[13]	27.33	4.45	47
文献[14]	22.01	3.63	47

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