Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network
The squelch problem of ultra-short wave communication under non-stationary noise and low Signal-to-Noise Ratio (SNR) in a complex electromagnetic environment is still challenging. To alleviate the problem, we proposed a squelch algorithm for ultra-short wave communication based on a deep neural netw...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-03-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020025 |
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| author | Yuanxin Xiang Yi Lv Wenqiang Lei Jiancheng Lv |
| author_facet | Yuanxin Xiang Yi Lv Wenqiang Lei Jiancheng Lv |
| author_sort | Yuanxin Xiang |
| collection | DOAJ |
| description | The squelch problem of ultra-short wave communication under non-stationary noise and low Signal-to-Noise Ratio (SNR) in a complex electromagnetic environment is still challenging. To alleviate the problem, we proposed a squelch algorithm for ultra-short wave communication based on a deep neural network and the traditional energy decision method. The proposed algorithm first predicts the speech existence probability using a three-layer Gated Recurrent Unit (GRU) with the speech banding spectrum as the feature. Then it gets the final squelch result by combining the strength of the signal energy and the speech existence probability. Multiple simulations and experiments are done to verify the robustness and effectiveness of the proposed algorithm. We simulate the algorithm in three situations: the typical Amplitude Modulation (AM) and Frequency Modulation (FM) in the ultra-short wave communication under different SNR environments, the non-stationary burst-like noise environments, and the real received signal of the ultra-short wave radio. The experimental results show that the proposed algorithm performs better than the traditional squelch methods in all the simulations and experiments. In particular, the false alarm rate of the proposed squelch algorithm for non-stationary burst-like noise is significantly lower than that of traditional squelch methods. |
| format | Article |
| id | doaj-art-f8e779f82f8a49f19c94c01b449774c5 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-f8e779f82f8a49f19c94c01b449774c52025-02-02T13:35:59ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-03-016110611410.26599/BDMA.2022.9020025Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural NetworkYuanxin Xiang0Yi Lv1Wenqiang Lei2Jiancheng Lv3College of Computer Science, Sichuan University, Chengdu 610000, ChinaSichuan Research Institute, Shanghai Jiao Tong University, Chengdu 610000, ChinaCollege of Computer Science, Sichuan University, Chengdu 610000, ChinaCollege of Computer Science, Sichuan University, Chengdu 610000, ChinaThe squelch problem of ultra-short wave communication under non-stationary noise and low Signal-to-Noise Ratio (SNR) in a complex electromagnetic environment is still challenging. To alleviate the problem, we proposed a squelch algorithm for ultra-short wave communication based on a deep neural network and the traditional energy decision method. The proposed algorithm first predicts the speech existence probability using a three-layer Gated Recurrent Unit (GRU) with the speech banding spectrum as the feature. Then it gets the final squelch result by combining the strength of the signal energy and the speech existence probability. Multiple simulations and experiments are done to verify the robustness and effectiveness of the proposed algorithm. We simulate the algorithm in three situations: the typical Amplitude Modulation (AM) and Frequency Modulation (FM) in the ultra-short wave communication under different SNR environments, the non-stationary burst-like noise environments, and the real received signal of the ultra-short wave radio. The experimental results show that the proposed algorithm performs better than the traditional squelch methods in all the simulations and experiments. In particular, the false alarm rate of the proposed squelch algorithm for non-stationary burst-like noise is significantly lower than that of traditional squelch methods.https://www.sciopen.com/article/10.26599/BDMA.2022.9020025squelchgated recurrent unit (gru)ultra-short wave communication |
| spellingShingle | Yuanxin Xiang Yi Lv Wenqiang Lei Jiancheng Lv Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network Big Data Mining and Analytics squelch gated recurrent unit (gru) ultra-short wave communication |
| title | Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network |
| title_full | Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network |
| title_fullStr | Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network |
| title_full_unstemmed | Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network |
| title_short | Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network |
| title_sort | ultra short wave communication squelch algorithm based on deep neural network |
| topic | squelch gated recurrent unit (gru) ultra-short wave communication |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020025 |
| work_keys_str_mv | AT yuanxinxiang ultrashortwavecommunicationsquelchalgorithmbasedondeepneuralnetwork AT yilv ultrashortwavecommunicationsquelchalgorithmbasedondeepneuralnetwork AT wenqianglei ultrashortwavecommunicationsquelchalgorithmbasedondeepneuralnetwork AT jianchenglv ultrashortwavecommunicationsquelchalgorithmbasedondeepneuralnetwork |