Paper Abstract and Keywords |
Presentation |
2020-08-21 10:30
[Encouragement Talk]
Doppler frequency estimation of Superimposed Control Signal of multiple satellites by Deep Learning in LEO-MIMO Ryo Okema (Nagoya Univ.), Daisuke Goto (NTT), Takaya Yamazato (Nagoya Univ.), Fumihiro Yamashita, Kiyohiko Itokawa (NTT) SAT2020-19 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
This paper focuses on satellite MIMO (Multi-Input Multi-Output) technology to increase the capacity of Low Earth Orbit (LEO) satellite communications.
In previous research, LEO-MIMO communication requires control signals to estimate the Doppler frequencies of each satellite.
The control signal is allocated a different frequency band to avoid inter-carrier interference with other signals.
However, as the number of communication LEO satellites increases, the bandwidth of the control signal becomes wider and the bandwidth for the MIMO signal becomes narrower.
In other words, the problem is that the capacity of LEO-MIMO decreases with the increase in the number of communication LEO satellites.
Therefore, this study attempts to suppress the communication capacity reduction by superimposing control signals.
This technique can improve the bandwidth of the control signal is constant regardless of the number of satellites, and suppress capacity reduction regardless the number of satellites.
On the other hands, superimposed control signals technique degrades the Doppler frequency estimation accuracy due to inter-carrier interference.
To tackle of the problem, we propose the novel Doppler frequency estimation using Deep learning techniques.
This paper deal with Doppler shift estimation for control signals with three or more superimposed waveforms and unlearned waveforms.
In order to improve the estimation accuracy, we employed convolutional neural networks in addition to deep neural networks and compared their performance. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Low earth orbit satellite / MIMO satellite communications / Deep learning / Signal detection / Doppler shift estimation / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 129, SAT2020-19, pp. 47-52, Aug. 2020. |
Paper # |
SAT2020-19 |
Date of Issue |
2020-08-13 (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) |
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SAT2020-19 |
Conference Information |
Committee |
SAT RCS |
Conference Date |
2020-08-20 - 2020-08-21 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
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Paper Information |
Registration To |
SAT |
Conference Code |
2020-08-SAT-RCS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Doppler frequency estimation of Superimposed Control Signal of multiple satellites by Deep Learning in LEO-MIMO |
Sub Title (in English) |
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Keyword(1) |
Low earth orbit satellite |
Keyword(2) |
MIMO satellite communications |
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Deep learning |
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Signal detection |
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Doppler shift estimation |
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1st Author's Name |
Ryo Okema |
1st Author's Affiliation |
Nagoya University (Nagoya Univ.) |
2nd Author's Name |
Daisuke Goto |
2nd Author's Affiliation |
NTT Access Network Service Systems Laboratories (NTT) |
3rd Author's Name |
Takaya Yamazato |
3rd Author's Affiliation |
Nagoya University (Nagoya Univ.) |
4th Author's Name |
Fumihiro Yamashita |
4th Author's Affiliation |
NTT Access Network Service Systems Laboratories (NTT) |
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Kiyohiko Itokawa |
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NTT Access Network Service Systems Laboratories (NTT) |
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Speaker |
Author-1 |
Date Time |
2020-08-21 10:30:00 |
Presentation Time |
25 minutes |
Registration for |
SAT |
Paper # |
SAT2020-19 |
Volume (vol) |
vol.120 |
Number (no) |
no.129 |
Page |
pp.47-52 |
#Pages |
6 |
Date of Issue |
2020-08-13 (SAT) |
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