基于深层神经网络的信道编码类型盲识别
TN911.7; 为了解决当前识别算法只能识别一种或者两种码字类型以及人工提取特征复杂的问题,提出了两种基于深层神经网络模型的信道编码类型识别器,即卷积神经网络(convolutional neural network,CNN)识别器和递归CNN(recursive CNN,RCNN)识别器,用于识别接收数据中不同类型的信道码字.将待识别的软解调序列作为自然语言处理中文本分类问题的句子向量进行处理,输入到预先训练好的深层神经网络识别器中进行识别,并分析了字长度对识别准确率的影响,得出了最合适的字长度.实验结果表明,两种识别器都能够有效识别接收数据中多种类型的信道编码,且在信噪比为3 dB时CN...
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| Published in | 系统工程与电子技术 Vol. 46; no. 5; pp. 1820 - 1829 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
重庆邮电大学信号与信息处理重庆市重点实验室,重庆 400065
01.05.2024
重庆邮电大学通信与信息工程学院,重庆 400065 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-506X |
| DOI | 10.12305/j.issn.1001-506X.2024.05.35 |
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| Abstract | TN911.7; 为了解决当前识别算法只能识别一种或者两种码字类型以及人工提取特征复杂的问题,提出了两种基于深层神经网络模型的信道编码类型识别器,即卷积神经网络(convolutional neural network,CNN)识别器和递归CNN(recursive CNN,RCNN)识别器,用于识别接收数据中不同类型的信道码字.将待识别的软解调序列作为自然语言处理中文本分类问题的句子向量进行处理,输入到预先训练好的深层神经网络识别器中进行识别,并分析了字长度对识别准确率的影响,得出了最合适的字长度.实验结果表明,两种识别器都能够有效识别接收数据中多种类型的信道编码,且在信噪比为3 dB时CNN识别器的识别准确率能够达到99％以上,而RCNN识别器在1 dB时就能够达到99％以上的识别准确率. |
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| AbstractList | TN911.7; 为了解决当前识别算法只能识别一种或者两种码字类型以及人工提取特征复杂的问题,提出了两种基于深层神经网络模型的信道编码类型识别器,即卷积神经网络(convolutional neural network,CNN)识别器和递归CNN(recursive CNN,RCNN)识别器,用于识别接收数据中不同类型的信道码字.将待识别的软解调序列作为自然语言处理中文本分类问题的句子向量进行处理,输入到预先训练好的深层神经网络识别器中进行识别,并分析了字长度对识别准确率的影响,得出了最合适的字长度.实验结果表明,两种识别器都能够有效识别接收数据中多种类型的信道编码,且在信噪比为3 dB时CNN识别器的识别准确率能够达到99％以上,而RCNN识别器在1 dB时就能够达到99％以上的识别准确率. |
| Abstract_FL | 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. |
| Author | 杨宗方 邹涵 张天骐 马焜然 |
| AuthorAffiliation | 重庆邮电大学通信与信息工程学院,重庆 400065;重庆邮电大学信号与信息处理重庆市重点实验室,重庆 400065 |
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| Author_FL | ZOU Han YANG Zongfang ZHANG Tianqi MA Kunran |
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| Keywords | channel code recognizer blind identification 信道编码识别器 字长度 word length 盲识别 深层神经网络 deep neural networks |
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| Publisher | 重庆邮电大学信号与信息处理重庆市重点实验室,重庆 400065 重庆邮电大学通信与信息工程学院,重庆 400065 |
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| Snippet | TN911.7; 为了解决当前识别算法只能识别一种或者两种码字类型以及人工提取特征复杂的问题,提出了两种基于深层神经网络模型的信道编码类型识别器,即卷积神经网络(convolutional neural network,CNN)识别器和递归CNN(recursive... |
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| Title | 基于深层神经网络的信道编码类型盲识别 |
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