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Paper Abstract and Keywords
Presentation 2022-11-24 10:45
Study on Training Data Generation for Estimating Spatial Loss Fields
Yoshiaki Nishikawa (NEC), Takahiro Matsuda (TMU), Eiji Takahashi, Takeo Onishi, Toshiki Takeuchi (NEC) CQ2022-47
Abstract (in Japanese) (See Japanese page) 
(in English) Spatial Loss Fields (SLFs) are maps quantifying the attenuation of radio signals in a monitored region. SLFs, which are estimated from received signal strengths, have some kind of unclearness coming from reflections and diffractions of signal. Although surpervised learning methods to decrease this unclearness need plenty of true SLFs, it is dificult to collect true SLFs of various factories. It is needed to generate training data which are torelant to the multipath environment. In this article, we propose training data generation method with a simulation. The proposed method makes true SLF randomly, and simulates whether received signals are transmitted on a line-of-sight (LOS) path or a non-line-of-sight path. The model is trained to infer the difference between true and estimated SLFs from the estimated SLF. We use denoise convolutional neaural network as the model and evaluate the performance of the proposed method with simulation experience.
Keyword (in Japanese) (See Japanese page) 
(in English) spatial loss field / denoise convolutional neaural network / surpervised learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 275, CQ2022-47, pp. 1-6, Nov. 2022.
Paper # CQ2022-47 
Date of Issue 2022-11-17 (CQ) 
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 CQ2022-47

Conference Information
Committee NS ICM CQ NV  
Conference Date 2022-11-24 - 2022-11-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Humanities and Social Sciences Center, Fukuoka Univ. + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network quality, Network measurement/management, Network virtualization, Network service, Blockchain, Security, Network intelligence/AI, etc. 
Paper Information
Registration To CQ 
Conference Code 2022-11-NS-ICM-CQ-NV 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Training Data Generation for Estimating Spatial Loss Fields 
Sub Title (in English)  
Keyword(1) spatial loss field  
Keyword(2) denoise convolutional neaural network  
Keyword(3) surpervised learning  
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1st Author's Name Yoshiaki Nishikawa  
1st Author's Affiliation NEC (NEC)
2nd Author's Name Takahiro Matsuda  
2nd Author's Affiliation Tokyo Metropolitan University (TMU)
3rd Author's Name Eiji Takahashi  
3rd Author's Affiliation NEC (NEC)
4th Author's Name Takeo Onishi  
4th Author's Affiliation NEC (NEC)
5th Author's Name Toshiki Takeuchi  
5th Author's Affiliation NEC (NEC)
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Speaker Author-1 
Date Time 2022-11-24 10:45:00 
Presentation Time 25 minutes 
Registration for CQ 
Paper # CQ2022-47 
Volume (vol) vol.122 
Number (no) no.275 
Page pp.1-6 
#Pages 6 
Date of Issue 2022-11-17 (CQ) 

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