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| Presentation | 2019-06-21 11:30 Damping Factor Learning of BP Detection with Node Selection in Massive MIMO using Neural Network Junta Tachibana, Tomoaki Ohtsuki (Keio Univ.)  RCS2019-93 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals.In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates deteriorates due to multiple loops included in the MIMO channel.As a method to improve the convergence property, the damped BP that averages the two successive messages with a weighing factor (called damping factor) is known.To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported.The problem with DNN-dBP is that the detection performance deteriorates due to the mismatches of the channel models between training and test, because the optimal damping factors vary with the channel correlation.In this report, to solve this issue, we propose the method combining DNN-dBP and the node selection (NS) method that selects nodes to be updated to lower spatial correlation.We train the damping factors of BP to which the NS method is applied.The mitigation of the effect of the channel correlation results in reducing the difference between the optimal values of the damping factors for each channel model. Therefore, the proposed method improves the detection performance in channels with various correlation values with one off-line training for each antenna configuration.By computer simulation, it is shown that the proposed method reduces the detection performance deterioration due to the mismatches of the channel models.The results also show that the proposed method has better detection performance than the conventional DNN-dBP not only in the fixed channel (FC) scenario but also in the time-varying channel (VC) scenario. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Massive MIMO / BP detection / Neural network / 5G / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 90, RCS2019-93, pp. 327-332, June 2019. | |
| Paper # | RCS2019-93 | |
| Date of Issue | 2019-06-12 (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 | RCS2019-93 |
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| Conference Information | |
| Committee | RCS |
| Conference Date | 2019-06-19 - 2019-06-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Miyakojima Hirara Port Terminal Building |
| 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 | 2019-06-RCS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Damping Factor Learning of BP Detection with Node Selection in Massive MIMO using Neural Network |
| Sub Title (in English) | |
| Keyword(1) | Massive MIMO |
| Keyword(2) | BP detection |
| Keyword(3) | Neural network |
| Keyword(4) | 5G |
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| 1st Author's Name | Junta Tachibana |
| 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 | 2019-06-21 11:30:00 |
| Presentation Time | 10 minutes |
| Registration for | RCS |
| Paper # | RCS2019-93 |
| Volume (vol) | vol.119 |
| Number (no) | no.90 |
| Page | pp.327-332 |
| #Pages | 6 |
| Date of Issue | 2019-06-12 (RCS) |
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