Underground signal recognition method based on higher-order cumulants and DNN model
In view of complex and heterogeneous wireless environment of mine, an underground signal recognition method based on higher-order cumulants and DNN model was proposed to realize automatic modulation recognition of underground digital signals of BPSK, QPSK, 8PSK, 2FSK, 4FSK, 8FSK, 32QAM, 64QAM, OFDM....
| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2020-02-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2019100064 |
| _version_ | 1819017959300923392 |
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| author | WANG Anyi LI Li |
| author_facet | WANG Anyi LI Li |
| author_sort | WANG Anyi |
| collection | DOAJ |
| description | In view of complex and heterogeneous wireless environment of mine, an underground signal recognition method based on higher-order cumulants and DNN model was proposed to realize automatic modulation recognition of underground digital signals of BPSK, QPSK, 8PSK, 2FSK, 4FSK, 8FSK, 32QAM, 64QAM, OFDM. Theoretical values of high-order cumulants of the 9 kinds of digital signals were obtained by analysis, and the signal identification was improved by Fourier transform. The influence of underground small-scale fading channels on high-order cumulants were analyzed, high-order cumulants calculation expression of the signal after passing through the underground channel was derived, and signal recognition was realized using characteristic parameters constructed according high-order cumulants to train DNN model. The simulation analysis results show that the method has excellent modulation recognition performance in mine Nakagami-m fading channel, average correct recognition rate is more than 89.2% when the signal-to-noise ratio is -5 dB, and the average correct recognition rate is 100% when the signal-to-noise ratio is 5 dB or more. The method provides a new idea for signal recognition and detection in special and complex environments. |
| first_indexed | 2024-12-21T03:11:48Z |
| format | Article |
| id | doaj.art-51f4a7d5a747424d9e03cb52ca655ab4 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-12-21T03:11:48Z |
| publishDate | 2020-02-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-51f4a7d5a747424d9e03cb52ca655ab42022-12-21T19:17:57ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2020-02-01462828710.13272/j.issn.1671-251x.2019100064Underground signal recognition method based on higher-order cumulants and DNN modelWANG AnyiLI LiIn view of complex and heterogeneous wireless environment of mine, an underground signal recognition method based on higher-order cumulants and DNN model was proposed to realize automatic modulation recognition of underground digital signals of BPSK, QPSK, 8PSK, 2FSK, 4FSK, 8FSK, 32QAM, 64QAM, OFDM. Theoretical values of high-order cumulants of the 9 kinds of digital signals were obtained by analysis, and the signal identification was improved by Fourier transform. The influence of underground small-scale fading channels on high-order cumulants were analyzed, high-order cumulants calculation expression of the signal after passing through the underground channel was derived, and signal recognition was realized using characteristic parameters constructed according high-order cumulants to train DNN model. The simulation analysis results show that the method has excellent modulation recognition performance in mine Nakagami-m fading channel, average correct recognition rate is more than 89.2% when the signal-to-noise ratio is -5 dB, and the average correct recognition rate is 100% when the signal-to-noise ratio is 5 dB or more. The method provides a new idea for signal recognition and detection in special and complex environments.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2019100064mine communicationunderground signal recognitionnakagami-m fading channelhigher-order cumulantdeep neural networkdnn model |
| spellingShingle | WANG Anyi LI Li Underground signal recognition method based on higher-order cumulants and DNN model Gong-kuang zidonghua mine communication underground signal recognition nakagami-m fading channel higher-order cumulant deep neural network dnn model |
| title | Underground signal recognition method based on higher-order cumulants and DNN model |
| title_full | Underground signal recognition method based on higher-order cumulants and DNN model |
| title_fullStr | Underground signal recognition method based on higher-order cumulants and DNN model |
| title_full_unstemmed | Underground signal recognition method based on higher-order cumulants and DNN model |
| title_short | Underground signal recognition method based on higher-order cumulants and DNN model |
| title_sort | underground signal recognition method based on higher order cumulants and dnn model |
| topic | mine communication underground signal recognition nakagami-m fading channel higher-order cumulant deep neural network dnn model |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2019100064 |
| work_keys_str_mv | AT wanganyi undergroundsignalrecognitionmethodbasedonhigherordercumulantsanddnnmodel AT lili undergroundsignalrecognitionmethodbasedonhigherordercumulantsanddnnmodel |