End-to-End PSK Signals Demodulation Using Convolutional Neural Network
Demodulation techniques are of central importance for achieving intelligent receiving. Improvement in demodulation performance enhances the overall performance of a communication system correspondingly. However, conventional demodulators require dedicated hardware platforms leading to high implement...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9775936/ |
| _version_ | 1828820819388661760 |
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| author | Wen-Jie Chen Jiao Wang Jian-Qing Li |
| author_facet | Wen-Jie Chen Jiao Wang Jian-Qing Li |
| author_sort | Wen-Jie Chen |
| collection | DOAJ |
| description | Demodulation techniques are of central importance for achieving intelligent receiving. Improvement in demodulation performance enhances the overall performance of a communication system correspondingly. However, conventional demodulators require dedicated hardware platforms leading to high implementation costs and time-consuming development. This work proposes a unified architecture for end-to-end automatic demodulated modulated signals. The proposed demodulator utilizes the residual unit and fully convolutional network (R-FCN) to extract the time-domain feature of the modulated signal and determine the transmitted symbols to realize the demodulation of a received signal. Simulations show that the proposed method has better demodulation performance compared to existing methods. It is further demonstrated that when the signal-to-noise ratios (SNR) exceed 2dB, the proposed demodulator exhibits similar demodulation performance to symbol-unsynchronized data compared to conventional demodulators. |
| first_indexed | 2024-12-12T12:41:46Z |
| format | Article |
| id | doaj.art-90008f11d9204a69ae8dd1f788c60ea2 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T12:41:46Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-90008f11d9204a69ae8dd1f788c60ea22022-12-22T00:24:12ZengIEEEIEEE Access2169-35362022-01-0110583025831010.1109/ACCESS.2022.31758619775936End-to-End PSK Signals Demodulation Using Convolutional Neural NetworkWen-Jie Chen0Jiao Wang1https://orcid.org/0000-0001-9439-3893Jian-Qing Li2https://orcid.org/0000-0001-9524-4291Southwest China Institute of Electronic Technology, Chengdu, ChinaSchool of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaDemodulation techniques are of central importance for achieving intelligent receiving. Improvement in demodulation performance enhances the overall performance of a communication system correspondingly. However, conventional demodulators require dedicated hardware platforms leading to high implementation costs and time-consuming development. This work proposes a unified architecture for end-to-end automatic demodulated modulated signals. The proposed demodulator utilizes the residual unit and fully convolutional network (R-FCN) to extract the time-domain feature of the modulated signal and determine the transmitted symbols to realize the demodulation of a received signal. Simulations show that the proposed method has better demodulation performance compared to existing methods. It is further demonstrated that when the signal-to-noise ratios (SNR) exceed 2dB, the proposed demodulator exhibits similar demodulation performance to symbol-unsynchronized data compared to conventional demodulators.https://ieeexplore.ieee.org/document/9775936/Convolutional neural networkdemodulationresidual network |
| spellingShingle | Wen-Jie Chen Jiao Wang Jian-Qing Li End-to-End PSK Signals Demodulation Using Convolutional Neural Network IEEE Access Convolutional neural network demodulation residual network |
| title | End-to-End PSK Signals Demodulation Using Convolutional Neural Network |
| title_full | End-to-End PSK Signals Demodulation Using Convolutional Neural Network |
| title_fullStr | End-to-End PSK Signals Demodulation Using Convolutional Neural Network |
| title_full_unstemmed | End-to-End PSK Signals Demodulation Using Convolutional Neural Network |
| title_short | End-to-End PSK Signals Demodulation Using Convolutional Neural Network |
| title_sort | end to end psk signals demodulation using convolutional neural network |
| topic | Convolutional neural network demodulation residual network |
| url | https://ieeexplore.ieee.org/document/9775936/ |
| work_keys_str_mv | AT wenjiechen endtoendpsksignalsdemodulationusingconvolutionalneuralnetwork AT jiaowang endtoendpsksignalsdemodulationusingconvolutionalneuralnetwork AT jianqingli endtoendpsksignalsdemodulationusingconvolutionalneuralnetwork |