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| Paper Abstract and Keywords | ||
| Presentation | 2023-08-31 10:30 Enhancing CSI Feedback in FDD Massive MIMO Systems using Dropout-based Deep Neural Network Junjie Gao, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.), Guan Gui (NJUPT)  RCS2023-101 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Accessing precise downlink channel state information (CSI) is crucial in maximizing the benefits of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, as they lack strong channel reciprocity. However, this requires continuous CSI feedback, leading to significant computational burdens. Existing compressive sensing (CS)-based and deep learning (DL)-based methods have attempted to address these challenges, but have not achieved the desired level of CSI feedback or overhead reduction. To overcome these limitations, a dropout-based deep neural network (DNN) is proposed in this paper. Simulation results demonstrate that the proposed method outperforms conventional approaches in terms of normalized mean square error (NMSE), even with a limited dataset in some noisy scenarios. These findings highlight the efficacy of the proposed method in improving CSI reconstruction accuracy and reducing the demand for training data, which enhance the robustness of the entire system. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | CSI feedback / deep neural network / classification / quantization / dropout / Massive MIMO / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 172, RCS2023-101, pp. 1-4, Aug. 2023. | |
| Paper # | RCS2023-101 | |
| Date of Issue | 2023-08-24 (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 | RCS2023-101 |
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| Conference Information | |
| Committee | RCS SAT |
| Conference Date | 2023-08-31 - 2023-09-01 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Naganoken Nokyo Building, and online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Satellite Communications, Broadcasting, Forward Error Correction, Wireless Communications, etc. |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2023-08-RCS-SAT |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Enhancing CSI Feedback in FDD Massive MIMO Systems using Dropout-based Deep Neural Network |
| Sub Title (in English) | |
| Keyword(1) | CSI feedback |
| Keyword(2) | deep neural network |
| Keyword(3) | classification |
| Keyword(4) | quantization |
| Keyword(5) | dropout |
| Keyword(6) | Massive MIMO |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Junjie Gao |
| 1st Author's Affiliation | Keio University (Keio Univ.) |
| 2nd Author's Name | Mondher Bouazizi |
| 2nd Author's Affiliation | Keio University (Keio Univ.) |
| 3rd Author's Name | Tomoaki Ohtsuki |
| 3rd Author's Affiliation | Keio University (Keio Univ.) |
| 4th Author's Name | Guan Gui |
| 4th Author's Affiliation | NJUPT (NJUPT) |
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| Speaker | Author-1 |
| Date Time | 2023-08-31 10:30:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | RCS2023-101 |
| Volume (vol) | vol.123 |
| Number (no) | no.172 |
| Page | pp.1-4 |
| #Pages | 4 |
| Date of Issue | 2023-08-24 (RCS) |
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