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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87930-8 |
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| Summary: | 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. |
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| ISSN: | 2045-2322 |