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Paper Abstract and Keywords
Presentation 2018-07-11 14:35
An evaluation of propagation loss on wireless physical layer identification
Taichi Ohtsuji, Tatsuya Soma, Toshiki Takeuchi, Masaki Kitsunezuka, Kazuaki Kunihiro (NEC) RCC2018-31 NS2018-44 RCS2018-86 SR2018-25 ASN2018-25
Abstract (in Japanese) (See Japanese page) 
(in English) Wireless devices have trivial individual differences due to hardware imperfections. Therefore, extracting unique features from the physical waveforms of wireless signals enables us to identify transmitter devices. It has been often assumed that radio sensors can receive high signal-to-noise ratio (SNR) waveforms in previous studies. On the other hand, there have been rare evaluation examples considering lower SNR environments. Classification accuracy could decrease in case the feature is extracted from signals with low SNR. In this paper, logarithmic power spectral density (PSD) is proposed as a radio feature to improve classification accuracy in lower SNR environment. The results obtained from simulations reveal that the proposed feature enables 12-dB higher to achieve 90% accuracy than the conventional feature. Furthermore, to decrease computational complexity, dimensional reduction of radio features based on principal component analysis is applied. Simulation result shows that dimension number of radio feature can reduce to approximately 1/50 with maintenance of accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Radio frequency fingerprinting / Power Spectral Density / Support Vector Machine / Principal Component Analysis / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 126, SR2018-25, pp. 25-31, July 2018.
Paper # SR2018-25 
Date of Issue 2018-07-04 (RCC, NS, RCS, SR, ASN) 
ISSN Print edition: ISSN 0913-5685    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 RCC2018-31 NS2018-44 RCS2018-86 SR2018-25 ASN2018-25

Conference Information
Committee ASN NS RCS SR RCC  
Conference Date 2018-07-11 - 2018-07-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Hakodate Arena 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Wireless Distributed Network, Machine Learning and AI for Wireless Communications and Networks, M2M (Machine-to-Machine), D2D (Device-to-Device), IoT（Internet of Things）, etc. 
Paper Information
Registration To SR 
Conference Code 2018-07-ASN-NS-RCS-SR-RCC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An evaluation of propagation loss on wireless physical layer identification 
Sub Title (in English)  
Keyword(1) Radio frequency fingerprinting  
Keyword(2) Power Spectral Density  
Keyword(3) Support Vector Machine  
Keyword(4) Principal Component Analysis  
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1st Author's Name Taichi Ohtsuji  
1st Author's Affiliation NEC Corporation (NEC)
2nd Author's Name Tatsuya Soma  
2nd Author's Affiliation NEC Corporation (NEC)
3rd Author's Name Toshiki Takeuchi  
3rd Author's Affiliation NEC Corporation (NEC)
4th Author's Name Masaki Kitsunezuka  
4th Author's Affiliation NEC Corporation (NEC)
5th Author's Name Kazuaki Kunihiro  
5th Author's Affiliation NEC Corporation (NEC)
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Speaker Author-1 
Date Time 2018-07-11 14:35:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # RCC2018-31, NS2018-44, RCS2018-86, SR2018-25, ASN2018-25 
Volume (vol) vol.118 
Number (no) no.123(RCC), no.124(NS), no.125(RCS), no.126(SR), no.127(ASN) 
Page pp.37-43(RCC), pp.43-49(NS), pp.37-43(RCS), pp.25-31(SR), pp.53-59(ASN) 
#Pages 7 
Date of Issue 2018-07-04 (RCC, NS, RCS, SR, ASN) 

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