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Paper Abstract and Keywords
Presentation 2022-01-25 11:15
An evaluation of CNN using Deep Residual Learning and Long Short-term Memory for LTE and WLAN Systems Classifications
Teruji Ide (NIT, Kagoshima college), Rozeha Rashid, M A Sarijari (UTM) SR2021-75
Abstract (in Japanese) (See Japanese page) 
(in English) In this study, we investigate and present a deep residual (ResNet) learning for modulation classification. The simulation results show the degradation problem that was exposed due to an increase in network depth and the saturation of accuracy in the modified conventional CNN; however, the proposed CNN has no such degradation. In addition, we propose another CNN architecture of the combination of CNN and LSTM (Long Short-term Memory). The processing burden of the conventional CNN is much larger than the proposed CNN ones. In the simulation results, the proposed CNN frameworks achieve almost the same system (LTE and WLAN) classification accuracy as the normal CNN framework when reducing the processing burden in the proposed ones. The better simulation results are shown by adjustment of the parameters using the proposed architectures (with ResNet and LSTM) for LTE and WLAN systems.
Keyword (in Japanese) (See Japanese page) 
(in English) CNN / residual learning / cognitive radio / modulation classification / system classification / LSTM / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 345, SR2021-75, pp. 82-89, Jan. 2022.
Paper # SR2021-75 
Date of Issue 2022-01-17 (SR) 
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 SR2021-75

Conference Information
Committee SR  
Conference Date 2022-01-24 - 2022-01-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SR 
Conference Code 2022-01-SR 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An evaluation of CNN using Deep Residual Learning and Long Short-term Memory for LTE and WLAN Systems Classifications 
Sub Title (in English)  
Keyword(1) CNN  
Keyword(2) residual learning  
Keyword(3) cognitive radio  
Keyword(4) modulation classification  
Keyword(5) system classification  
Keyword(6) LSTM  
Keyword(7)  
Keyword(8)  
1st Author's Name Teruji Ide  
1st Author's Affiliation National Institute of Technology, Kagoshima college (NIT, Kagoshima college)
2nd Author's Name Rozeha Rashid  
2nd Author's Affiliation University Technology Malaysia (UTM)
3rd Author's Name M A Sarijari  
3rd Author's Affiliation University Technology Malaysia (UTM)
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Speaker Author-1 
Date Time 2022-01-25 11:15:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2021-75 
Volume (vol) vol.121 
Number (no) no.345 
Page pp.82-89 
#Pages 8 
Date of Issue 2022-01-17 (SR) 

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