Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network
The accurate identification of channel-coding types plays a crucial role in wireless communication systems. The recognition of convolutional codes presents challenges, primarily due to their strong temporal dependencies, varying constraint lengths, and additional contamination from noise. However, e...
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/4/1000 |
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| author | Lu Xu Yixin Ma Rui Shi Juanjuan Li Yijia Zhang |
| author_facet | Lu Xu Yixin Ma Rui Shi Juanjuan Li Yijia Zhang |
| author_sort | Lu Xu |
| collection | DOAJ |
| description | The accurate identification of channel-coding types plays a crucial role in wireless communication systems. The recognition of convolutional codes presents challenges, primarily due to their strong temporal dependencies, varying constraint lengths, and additional contamination from noise. However, existing algorithms often rely on manual feature extraction or are limited to a restricted number of coding types, rendering them inadequate for practical applications. To tackle this problem, we propose ConvLSTM-TFN (temporal feature network), an innovative blind-recognition network that integrates convolutional layers, long short-term memory (LSTM) networks, and a self-attention mechanism. The proposed approach enhances the acquisition of features from soft-decision sequence information, leading to improved recognition performance without necessitating prior knowledge of coding parameters, sequence starting positions, or other metadata. The experimental results demonstrate that our method is effective within a signal-to-noise ratio (SNR) range of 0 to 20 dB, achieving more than 90% recognition accuracy across 17 convolutional code types, with an average accuracy of 98.7%. Our method effectively distinguishes diverse coding features, surpassing existing models and establishing a new benchmark for channel-coding recognition. |
| format | Article |
| id | doaj-art-ebdce971deca4871a8fc9698bdd93dce |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ebdce971deca4871a8fc9698bdd93dce2025-08-20T02:04:07ZengMDPI AGSensors1424-82202025-02-01254100010.3390/s25041000Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature NetworkLu Xu0Yixin Ma1Rui Shi2Juanjuan Li3Yijia Zhang4School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaThe accurate identification of channel-coding types plays a crucial role in wireless communication systems. The recognition of convolutional codes presents challenges, primarily due to their strong temporal dependencies, varying constraint lengths, and additional contamination from noise. However, existing algorithms often rely on manual feature extraction or are limited to a restricted number of coding types, rendering them inadequate for practical applications. To tackle this problem, we propose ConvLSTM-TFN (temporal feature network), an innovative blind-recognition network that integrates convolutional layers, long short-term memory (LSTM) networks, and a self-attention mechanism. The proposed approach enhances the acquisition of features from soft-decision sequence information, leading to improved recognition performance without necessitating prior knowledge of coding parameters, sequence starting positions, or other metadata. The experimental results demonstrate that our method is effective within a signal-to-noise ratio (SNR) range of 0 to 20 dB, achieving more than 90% recognition accuracy across 17 convolutional code types, with an average accuracy of 98.7%. Our method effectively distinguishes diverse coding features, surpassing existing models and establishing a new benchmark for channel-coding recognition.https://www.mdpi.com/1424-8220/25/4/1000channel-coding recognitionconvolutional codeswireless communicationtemporal feature networkdeep learning |
| spellingShingle | Lu Xu Yixin Ma Rui Shi Juanjuan Li Yijia Zhang Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network Sensors channel-coding recognition convolutional codes wireless communication temporal feature network deep learning |
| title | Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network |
| title_full | Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network |
| title_fullStr | Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network |
| title_full_unstemmed | Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network |
| title_short | Blind Recognition of Convolutional Codes Based on the ConvLSTM Temporal Feature Network |
| title_sort | blind recognition of convolutional codes based on the convlstm temporal feature network |
| topic | channel-coding recognition convolutional codes wireless communication temporal feature network deep learning |
| url | https://www.mdpi.com/1424-8220/25/4/1000 |
| work_keys_str_mv | AT luxu blindrecognitionofconvolutionalcodesbasedontheconvlstmtemporalfeaturenetwork AT yixinma blindrecognitionofconvolutionalcodesbasedontheconvlstmtemporalfeaturenetwork AT ruishi blindrecognitionofconvolutionalcodesbasedontheconvlstmtemporalfeaturenetwork AT juanjuanli blindrecognitionofconvolutionalcodesbasedontheconvlstmtemporalfeaturenetwork AT yijiazhang blindrecognitionofconvolutionalcodesbasedontheconvlstmtemporalfeaturenetwork |