Abstract
This paper proposes a convolutional neural network (CNN)-based Doppler shift estimation model for low earth orbit (LEO) satellite communication systems. We propose a deep learning model which estimates the Doppler shift caused by the high orbital velocity of the satellite. The proposed model extracts the channel feature with the CNN and is trained to minimize the error with the ground truth Doppler shift value. In numerical results, it is shown that the proposed model can accurately estimate the Doppler shift that the satellite communication channel experienced.
| Original language | English |
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| Title of host publication | ITC-CSCC 2022 - 37th International Technical Conference on Circuits/Systems, Computers and Communications |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 717-719 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781665485593 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 37th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2022 - Phuket, Thailand Duration: 2022 Jul 5 → 2022 Jul 8 |
Publication series
| Name | ITC-CSCC 2022 - 37th International Technical Conference on Circuits/Systems, Computers and Communications |
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Conference
| Conference | 37th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2022 |
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| Country/Territory | Thailand |
| City | Phuket |
| Period | 22/7/5 → 22/7/8 |
Bibliographical note
Funding Information:This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C1011443).
Publisher Copyright:
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Information Systems
- Electrical and Electronic Engineering
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Hardware and Architecture