Paper Abstract and Keywords |
Presentation |
2020-03-04 12:15
[Technology Exhibit]
Utilizing Deep Learning for Wireless Communications
-- Modulation Method Classification using CNN -- Akemi Tanaka (MathWorks) RCS2019-329 SR2019-113 SRW2019-59 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In recent years, deep learning has been applied and put to practical use in various fields. Application to wireless communication systems is also being studied but the problem that it cannot be tried immediately because of the need for specialized knowledge and labor and few examples of handling time-series data. Therefore, this paper describes an example of application to a wireless communication system using the workflow of deep learning provided by MATLAB®. Specifically, we introduce examples of classifying signal waveforms modulated by various modulation methods using CNN, which is a deep neural network for images. In addition, we evaluate the classification performance by simulation and describe the experimental results of classification using real signals using the software defined radio ADALM-PLUTO. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Software defined radio / Wireless communication / Deep Learning / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 448, RCS2019-329, pp. 51-54, March 2020. |
Paper # |
RCS2019-329 |
Date of Issue |
2020-02-26 (RCS, SR, SRW) |
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 |
RCS2019-329 SR2019-113 SRW2019-59 |
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) |
Utilizing Deep Learning for Wireless Communications |
Sub Title (in English) |
Modulation Method Classification using CNN |
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Software defined radio |
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Wireless communication |
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Deep Learning |
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1st Author's Name |
Akemi Tanaka |
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MathWorks (MathWorks) |
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Speaker |
Author-1 |
Date Time |
2020-03-04 12:15:00 |
Presentation Time |
20 minutes |
Registration for |
RCS |
Paper # |
RCS2019-329, SR2019-113, SRW2019-59 |
Volume (vol) |
vol.119 |
Number (no) |
no.448(RCS), no.449(SR), no.450(SRW) |
Page |
pp.51-54(RCS), pp.3-6(SR), pp.3-6(SRW) |
#Pages |
4 |
Date of Issue |
2020-02-26 (RCS, SR, SRW) |
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