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| Paper Abstract and Keywords | ||
| Presentation | 2023-03-01 10:00 Continual Learning and Deep Transfer Learning Based CSI Feedback in FDD Massive MIMO Systems Mayuko Inoue, Tomoaki Ohtsuki (Keio Univ.)  RCS2022-251 |
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| 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). In this method, a source model trained on the CSI dataset (source data) in a given channel environment can be fine-tuned with a small number of CSI dataset (target data) in the target channel environment to obtain a target model at a low cost. However, the performance the target model obtained by DTL achieves with the source data degrades largely compared to that of the source model. Therefore, in realistic time-varying channel environments, which may change again into the source data channel environment, the target model must relearn the source data. To solve this issue, we propose a method that combines the CSI feedback method based on DTL with continual learning. The elastic weight consolidation (EWC) was introduced to the loss function during fine tuning. Simulation results show that our method significantly reduces the degradation of the NMSE that the target model achieves with the source data compared to the case without continual learning. The target model obtained by the proposed method with a mixed dataset of five different channel environments as the source data tends to have good performance regardless of the channel environments. These results indicate that the proposed method can reduce the relearning cost of the target model and can be useful in realistic time-varying channel environments. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | CSI feedback / DTL / continual learning / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 399, RCS2022-251, pp. 25-30, March 2023. | |
| Paper # | RCS2022-251 | |
| Date of Issue | 2023-02-22 (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) | |
| Download PDF | RCS2022-251 |
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| Conference Information | |
| Committee | RCS SR SRW |
| Conference Date | 2023-03-01 - 2023-03-03 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo Institute of Technology, and Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2023-03-RCS-SR-SRW |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Continual Learning and Deep Transfer Learning Based CSI Feedback in FDD Massive MIMO Systems |
| Sub Title (in English) | |
| Keyword(1) | CSI feedback |
| Keyword(2) | DTL |
| Keyword(3) | continual learning |
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| 1st Author's Name | Mayuko Inoue |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Tomoaki Ohtsuki |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
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| Speaker | Author-1 |
| Date Time | 2023-03-01 10:00:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCS2022-251 |
| Volume (vol) | vol.122 |
| Number (no) | no.399 |
| Page | pp.25-30 |
| #Pages | 6 |
| Date of Issue | 2023-02-22 (RCS) |
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