Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram
Automatic modulation recognition is a key technology in the field of signal processing. Conventional recognition methods suffer from low recognition accuracy at low signal-to-noise ratios (SNR), and when the signal frequency is unstable or there is asynchronous sampling, the performance of conventio...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2024/9589239 |
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| _version_ | 1832544679526137856 |
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| author | Xu Zhang Xi Hui Pengwu Wan Tengfei Hui Xiongfei Li |
| author_facet | Xu Zhang Xi Hui Pengwu Wan Tengfei Hui Xiongfei Li |
| author_sort | Xu Zhang |
| collection | DOAJ |
| description | Automatic modulation recognition is a key technology in the field of signal processing. Conventional recognition methods suffer from low recognition accuracy at low signal-to-noise ratios (SNR), and when the signal frequency is unstable or there is asynchronous sampling, the performance of conventional recognition methods will deteriorate or even fail. To address these challenges, deep learning-based modulation mode recognition technique is investigated in this paper for low-speed asynchronous sampled signals under channel conditions with varying SNR and delay. Firstly, the low-speed asynchronous sampled signals are modeled, and their in-phase quadrature components are used to generate a two-dimensional asynchronous in-phase quadrature histogram. Then, the feature parameters of this 2D image are extracted by radial basis function neural network (RBFNN) to complete the recognition of the modulation mode of the input signal. Finally, the accuracy of the method for seven modulation methods is verified by extensive simulations. The experimental results show that under the channel model of additive white Gaussian noise (AWGN), when the SNR of the input signal with low-speed asynchronous sampling is 6 dB, more than 95% of the average recognition accuracy can be achieved, and the effectiveness and robustness of the proposed scheme are verified by comparative experiments. |
| format | Article |
| id | doaj-art-49af540a2754426baf08b3f005d9e9a3 |
| institution | Kabale University |
| issn | 1751-9683 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-49af540a2754426baf08b3f005d9e9a32025-02-03T10:02:11ZengWileyIET Signal Processing1751-96832024-01-01202410.1049/2024/9589239Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature HistogramXu Zhang0Xi Hui1Pengwu Wan2Tengfei Hui3Xiongfei Li4National Key Laboratory of Science and Technology on Space MicrowaveXI’AN University of Posts and TelecommunicationsXI’AN University of Posts and TelecommunicationsChina Academy of Space TechnologyChina Academy of Space TechnologyAutomatic modulation recognition is a key technology in the field of signal processing. Conventional recognition methods suffer from low recognition accuracy at low signal-to-noise ratios (SNR), and when the signal frequency is unstable or there is asynchronous sampling, the performance of conventional recognition methods will deteriorate or even fail. To address these challenges, deep learning-based modulation mode recognition technique is investigated in this paper for low-speed asynchronous sampled signals under channel conditions with varying SNR and delay. Firstly, the low-speed asynchronous sampled signals are modeled, and their in-phase quadrature components are used to generate a two-dimensional asynchronous in-phase quadrature histogram. Then, the feature parameters of this 2D image are extracted by radial basis function neural network (RBFNN) to complete the recognition of the modulation mode of the input signal. Finally, the accuracy of the method for seven modulation methods is verified by extensive simulations. The experimental results show that under the channel model of additive white Gaussian noise (AWGN), when the SNR of the input signal with low-speed asynchronous sampling is 6 dB, more than 95% of the average recognition accuracy can be achieved, and the effectiveness and robustness of the proposed scheme are verified by comparative experiments.http://dx.doi.org/10.1049/2024/9589239 |
| spellingShingle | Xu Zhang Xi Hui Pengwu Wan Tengfei Hui Xiongfei Li Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram IET Signal Processing |
| title | Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram |
| title_full | Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram |
| title_fullStr | Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram |
| title_full_unstemmed | Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram |
| title_short | Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram |
| title_sort | asynchronous wireless signal modulation recognition based on in phase quadrature histogram |
| url | http://dx.doi.org/10.1049/2024/9589239 |
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