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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Wen Liu Shixuan Zheng Zhongliang Deng Ke Wang Wenliang Lin Jizhao Lei Yihui Jin Hao Liu |
| Abstract | The modeling of low earth orbit (LEO) satellite channel depends on its Doppler power spectrum. Due to satellite during transit, diversity and dynamic channel scene, the modeling of Doppler power spectrum has two serious problems: one is that the shape of the Doppler power spectrum will vary with the change of scenes and time, but the use of the existing traditional Doppler power spectrum models is difficult to accurately describe them. The other one is that the amount of measured data used for modeling is too large to handle, and data aliasing is easy to occur between different scenes, which will make it difficult to ensure the accuracy of model parameter fitting. In this paper, a two-side truncated asymmetric Doppler power spectrum model is proposed to universally describe the Doppler power spectrum during satellite transit. In addition, the atlas fingerprint method clustering is adopted to realize the classification of the measured data samples of Doppler power spectrum in multiple scenes, and the data with the strongest representation ability in each scene is selected to fit the model parameters. The simulation results show that the proposed model is in good agreement with the measured data. Therefore, parameter fitting using the proposed method can improve the accuracy of the model, so as to better describe the characteristics of LEO satellite channel fading in the frequency domain. |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2021.3051774 |
| Journal | IEEE Access |
| Volume Number | 9 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2021-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Leo Satellite Channel Doppler Power Spectrum Model Multiple Scenes Atlas Fingerprint Parameter Fitting |
| Content Type | Text |
| Resource Type | Article |
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