Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning
Abstract Studying the propagation prediction of low-frequency (LF) radio waves is very significant for supporting applications in fixed and mobile long-distance communication, remote navigation, and timing service. Therefore, to enhance the predicting accuracy of LF sky wave propagation, we proposed...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87930-8 |
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| _version_ | 1823862525621436416 |
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| author | Jian Wang Chengsong Duan Qiao Yu Cheng Yang |
| author_facet | Jian Wang Chengsong Duan Qiao Yu Cheng Yang |
| author_sort | Jian Wang |
| collection | DOAJ |
| description | Abstract Studying the propagation prediction of low-frequency (LF) radio waves is very significant for supporting applications in fixed and mobile long-distance communication, remote navigation, and timing service. Therefore, to enhance the predicting accuracy of LF sky wave propagation, we proposed an improved method based on the machine learning method. Firstly, we employed a machine learning method to create a prediction model for the critical frequency of the low ionospheric E layer (f oE), which significantly affects LF sky wave propagation. Secondly, we enhanced the method for predicting LF sky wave propagation based on the model of low ionospheric parameters. By comparing the measured data from East Asia and predicted data based on the wave-hop theory, the proposed method achieved a 6.16% improvement in LF sky wave field strength. |
| format | Article |
| id | doaj-art-e0ef83d59e0049ad97fd09d07445d31a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e0ef83d59e0049ad97fd09d07445d31a2025-02-09T12:31:49ZengNature PortfolioScientific Reports2045-23222025-02-0115111610.1038/s41598-025-87930-8Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learningJian Wang0Chengsong Duan1Qiao Yu2Cheng Yang3School of Microelectronics, Tianjin UniversitySchool of Microelectronics, Tianjin UniversitySchool of Microelectronics, Tianjin UniversitySchool of Microelectronics, Tianjin UniversityAbstract Studying the propagation prediction of low-frequency (LF) radio waves is very significant for supporting applications in fixed and mobile long-distance communication, remote navigation, and timing service. Therefore, to enhance the predicting accuracy of LF sky wave propagation, we proposed an improved method based on the machine learning method. Firstly, we employed a machine learning method to create a prediction model for the critical frequency of the low ionospheric E layer (f oE), which significantly affects LF sky wave propagation. Secondly, we enhanced the method for predicting LF sky wave propagation based on the model of low ionospheric parameters. By comparing the measured data from East Asia and predicted data based on the wave-hop theory, the proposed method achieved a 6.16% improvement in LF sky wave field strength.https://doi.org/10.1038/s41598-025-87930-8Ionospheric f oELFMachine learningSky wave propagation prediction |
| spellingShingle | Jian Wang Chengsong Duan Qiao Yu Cheng Yang Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning Scientific Reports Ionospheric f oE LF Machine learning Sky wave propagation prediction |
| title | Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning |
| title_full | Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning |
| title_fullStr | Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning |
| title_full_unstemmed | Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning |
| title_short | Predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning |
| title_sort | predicting low ionospheric parameters and low frequency sky wave propagation strength using machine learning |
| topic | Ionospheric f oE LF Machine learning Sky wave propagation prediction |
| url | https://doi.org/10.1038/s41598-025-87930-8 |
| work_keys_str_mv | AT jianwang predictinglowionosphericparametersandlowfrequencyskywavepropagationstrengthusingmachinelearning AT chengsongduan predictinglowionosphericparametersandlowfrequencyskywavepropagationstrengthusingmachinelearning AT qiaoyu predictinglowionosphericparametersandlowfrequencyskywavepropagationstrengthusingmachinelearning AT chengyang predictinglowionosphericparametersandlowfrequencyskywavepropagationstrengthusingmachinelearning |