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| Presentation | 2020-03-04 09:20 CSI Feedback Overhead Reduction by 3D CNN for Time-varying FDD Massive MIMO Masumi Kuriyama, Tomoaki Ohtsuki (Keio Univ.)  RCS2019-332 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Massive MIMO (Multiple-Input Multiple-Output) is a technology that uses a large number of antennas at a base station (BS), thereby achieving high communication performance. However, massive MIMO has a problem that the feedback of channel state information (CSI) required for precoding in the BS increases due to the large number of antennas. Recently, there is a technique of using deep learning to address this problem. In the conventional method using deep learning, features are extracted by treating CSI matrices represented by space and frequency as images, and are used for compression and reconstruction. In this report, we propose a method that uses a 3-dimensional convolutional neural network (3D CNN) to extract features in the time domain of CSI, in addition to the spatial and frequency domains, and to perform compression and reconstruction. Furthermore, the proposed method uses prediction by Convolutional LSTM (ConvLSTM), a kind of recurrent neural network (RNN), to compensate for the difference between the reconstructed CSI and that required for precoding due to the feedback delay of the time-varying channel. The proposed method achieves high accuracy by CSI compression /reconstruction using 3D CNN and channel prediction using ConvLSTM, even if information is compressed 1/64. Also, the proposed method improves the reconstruction accuracy at an arbitrary compression ratio compared to the conventional one that learns only two dimensions of space and frequency. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Massive MIMO / channel feedback / 3D CNN / ConvLSTM / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 448, RCS2019-332, pp. 63-68, March 2020. | |
| Paper # | RCS2019-332 | |
| Date of Issue | 2020-02-26 (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 | RCS2019-332 |
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| Conference Information | |
| Committee | RCS SR SRW |
| Conference Date | 2020-03-04 - 2020-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokyo Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Mobile Communication Workshop |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2020-03-RCS-SR-SRW |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | CSI Feedback Overhead Reduction by 3D CNN for Time-varying FDD Massive MIMO |
| Sub Title (in English) | |
| Keyword(1) | Massive MIMO |
| Keyword(2) | channel feedback |
| Keyword(3) | 3D CNN |
| Keyword(4) | ConvLSTM |
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| 1st Author's Name | Masumi Kuriyama |
| 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 | 2020-03-04 09:20:00 |
| Presentation Time | 20 minutes |
| Registration for | RCS |
| Paper # | RCS2019-332 |
| Volume (vol) | vol.119 |
| Number (no) | no.448 |
| Page | pp.63-68 |
| #Pages | 6 |
| Date of Issue | 2020-02-26 (RCS) |
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