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| Presentation | 2022-08-25 15:50 [Encouragement Talk] Machine Learning Based Digital Signal Processing for Satellite Communication Systems Manabu Takagi, Yasunori Noda, Masatake Hangai, Masaki Noda (Mitsubishi Electric Corp.)  SAT2022-35 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Digitization of satellite payloads is a big trend. One example is a satellite with a fully digitized onboard processor called the Software Defined Satellite. Machine learning is compatible with these digitized on-board processing, and by constructing the digital signal processing of satellite systems based on machine learning, it is possible to further improve flexibility. As examples of machine learning for satellite systems, we proposed two methods. First, the blind center frequency estimation method that does not require a pilot signal. Second, the signal multiplexing for navigation signals. It was confirmed that the blind center frequency estimation of machine learning has a performance of 90% or more regardless of the modulation method. It was confirmed that the machine learning based signal multiplexing is superior in both performance and flexibility to the POCET method. When the number of signal multiplex increases due to a change in the operation scenario of the satellite system, the POCET method has been found to cause up to about 85% signal degradation, while the proposed method causes up to 16% signal degradation. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Satellite Communications / Frequency Estimation / Navigation Signal / Signal Multiplexing / Machine Learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 163, SAT2022-35, pp. 36-41, Aug. 2022. | |
| Paper # | SAT2022-35 | |
| Date of Issue | 2022-08-18 (SAT) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | SAT2022-35 |
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| Conference Information | |
| Committee | SAT RCS |
| Conference Date | 2022-08-25 - 2022-08-26 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | SAT |
| Conference Code | 2022-08-SAT-RCS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Machine Learning Based Digital Signal Processing for Satellite Communication Systems |
| Sub Title (in English) | |
| Keyword(1) | Satellite Communications |
| Keyword(2) | Frequency Estimation |
| Keyword(3) | Navigation Signal |
| Keyword(4) | Signal Multiplexing |
| Keyword(5) | Machine Learning |
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| Keyword(8) | |
| 1st Author's Name | Manabu Takagi |
| 1st Author's Affiliation | Mitsubishi Electric Corporation (Mitsubishi Electric Corp.) |
| 2nd Author's Name | Yasunori Noda |
| 2nd Author's Affiliation | Mitsubishi Electric Corporation (Mitsubishi Electric Corp.) |
| 3rd Author's Name | Masatake Hangai |
| 3rd Author's Affiliation | Mitsubishi Electric Corporation (Mitsubishi Electric Corp.) |
| 4th Author's Name | Masaki Noda |
| 4th Author's Affiliation | Mitsubishi Electric Corporation (Mitsubishi Electric Corp.) |
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| Speaker | Author-1 |
| Date Time | 2022-08-25 15:50:00 |
| Presentation Time | 25 minutes |
| Registration for | SAT |
| Paper # | SAT2022-35 |
| Volume (vol) | vol.122 |
| Number (no) | no.163 |
| Page | pp.36-41 |
| #Pages | 6 |
| Date of Issue | 2022-08-18 (SAT) |
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