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| Presentation | 2022-01-20 14:05 Automatic Modulation Classification Based on SNR estimation using Multi-Task Learning Wataru Machida, Yosuke Sugiura, Nozomiko Yasui, Tetsuya Shimamura (Saitama Univ.)  IT2021-56 SIP2021-64 RCS2021-224 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Automatic modulation classification is a technology that identifies the modulation type used in received signals and plays an important role in wireless communication. The purpose of this study is to improve the accuracy of the automatic modulation classification model by using multi-task learning architecture with two input formats, which are I/Q sample and A/P sample. Based on the conventional model, we propose a new model solving an auxiliary task of SNR estimation and incorporating an Attention mechanism. From the results of the evaluation experiment, we confirm that the proposed method can improve the accuracy of the automatic modulation classification. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Automatic modulation classification / Multi-task learning / SNR estimation / Attention Mechanism / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 329, RCS2021-224, pp. 155-160, Jan. 2022. | |
| Paper # | RCS2021-224 | |
| Date of Issue | 2022-01-13 (IT, SIP, 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 | IT2021-56 SIP2021-64 RCS2021-224 |
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| Conference Information | |
| Committee | RCS SIP IT |
| Conference Date | 2022-01-20 - 2022-01-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2022-01-RCS-SIP-IT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Automatic Modulation Classification Based on SNR estimation using Multi-Task Learning |
| Sub Title (in English) | |
| Keyword(1) | Automatic modulation classification |
| Keyword(2) | Multi-task learning |
| Keyword(3) | SNR estimation |
| Keyword(4) | Attention Mechanism |
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| 1st Author's Name | Wataru Machida |
| 1st Author's Affiliation | Saitama University (Saitama Univ.) |
| 2nd Author's Name | Yosuke Sugiura |
| 2nd Author's Affiliation | Saitama University (Saitama Univ.) |
| 3rd Author's Name | Nozomiko Yasui |
| 3rd Author's Affiliation | Saitama University (Saitama Univ.) |
| 4th Author's Name | Tetsuya Shimamura |
| 4th Author's Affiliation | Saitama University (Saitama Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-01-20 14:05:00 |
| Presentation Time | 25 minutes |
| Registration for | RCS |
| Paper # | IT2021-56, SIP2021-64, RCS2021-224 |
| Volume (vol) | vol.121 |
| Number (no) | no.327(IT), no.328(SIP), no.329(RCS) |
| Page | pp.155-160 |
| #Pages | 6 |
| Date of Issue | 2022-01-13 (IT, SIP, RCS) |
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