Paper Abstract and Keywords |
Presentation |
2022-06-17 13:40
Evaluation of Source Data Selection for CSI Feedback Method based on Deep Transfer Learning in FDD Massive MIMO Systems Mayuko Inoue, Tomoaki Ohtsuki (Keio Univ) RCS2022-62 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO), the downlink channel state information (CSI) feedback method based on deep transfer learning (DTL) has been proposed to obtain the downlink CSI at the Base Station (BS). This method can obtain a target model at low training cost by fine-tuning a source model trained on a large amount of CSI data set (source data) for a certain channel environment, with a small amount of CSI data set (target data) for the target channel environment. However, the performance of the target model could highly depend on the source data. In this paper, we investigate two metrics as criteria for selecting source data to obtain a target model with a high CSI reconstruction performance: (i) Jensen-Shannon Divergence (JSD) which represents the similarity between target and source data, and (ii) entropy, which represents the diversity of source data. The simulation results showed when the target channel model is non line-of-sight (NLOS), the source data with high entropy and low JSD tend to provide higher CSI reconstruction performance of the target model. These results indicate that the JSD and the entropy could be a source data selection metric. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
CSI Feedback / Deep Transfer Learning / Source Data Selection / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 73, RCS2022-62, pp. 224-229, June 2022. |
Paper # |
RCS2022-62 |
Date of Issue |
2022-06-08 (RCS) |
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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RCS2022-62 |
Conference Information |
Committee |
RCS |
Conference Date |
2022-06-15 - 2022-06-17 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
University of the Ryukyus, Senbaru Campus and online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
First Presentation in IEICE Technical Committee, Resource Control, Scheduling, Wireless Communications, etc. |
Paper Information |
Registration To |
RCS |
Conference Code |
2022-06-RCS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Evaluation of Source Data Selection for CSI Feedback Method based on Deep Transfer Learning in FDD Massive MIMO Systems |
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CSI Feedback |
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Deep Transfer Learning |
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Source Data Selection |
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1st Author's Name |
Mayuko Inoue |
1st Author's Affiliation |
Keio University (Keio Univ) |
2nd Author's Name |
Tomoaki Ohtsuki |
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Keio University (Keio Univ) |
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Speaker |
Author-1 |
Date Time |
2022-06-17 13:40:00 |
Presentation Time |
20 minutes |
Registration for |
RCS |
Paper # |
RCS2022-62 |
Volume (vol) |
vol.122 |
Number (no) |
no.73 |
Page |
pp.224-229 |
#Pages |
6 |
Date of Issue |
2022-06-08 (RCS) |
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