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Paper Abstract and Keywords
Presentation 2018-07-12 10:55
[Poster Presentation] Deep Learning Based RSS Prediction Using RGB-D Camera for mmWave Communications
Kota Nakashima, Yusuke Koda, Koji Yamamoto, Hironao Okamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.) RCC2018-37 NS2018-50 RCS2018-95 SR2018-34 ASN2018-31
Abstract (in Japanese) (See Japanese page) 
(in English) This paper experimentally finds the optimum number of input images of a machine learning-based mmWave received signal strength (RSS) value prediction scheme from depth images. By modeling the relationships between time-sequential depth images and RSS values based on machine learning, it is possible to predict the future RSS values, and thereby, a predictive handover makes a moment of degradation of the RSS value avoidable. As prediction models of RSS value, two machine learning models are compared: the combination of the convolutional neural network and convolutional long short-term memory (CNN+ConvLSTM), and random forest. As the number of input images increases, the prediction accuracy generally improves, however, too numerous input images may make the prediction accuracy worse because of over-fitting. Experimental results reveal that the number of input images that are input in order to predict the RSS value the most accurately is 16.
Keyword (in Japanese) (See Japanese page) 
(in English) machine learning / deep learning / wireless communication quality prediction / millimeter-wave communications / handover / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 127, ASN2018-31, pp. 91-92, July 2018.
Paper # ASN2018-31 
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 RCC2018-37 NS2018-50 RCS2018-95 SR2018-34 ASN2018-31

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 ASN 
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) Deep Learning Based RSS Prediction Using RGB-D Camera for mmWave Communications 
Sub Title (in English)  
Keyword(1) machine learning  
Keyword(2) deep learning  
Keyword(3) wireless communication quality prediction  
Keyword(4) millimeter-wave communications  
Keyword(5) handover  
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1st Author's Name Kota Nakashima  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Yusuke Koda  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Koji Yamamoto  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Hironao Okamoto  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
5th Author's Name Takayuki Nishio  
5th Author's Affiliation Kyoto University (Kyoto Univ.)
6th Author's Name Masahiro Morikura  
6th Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2018-07-12 10:55:00 
Presentation Time 80 minutes 
Registration for ASN 
Paper # RCC2018-37, NS2018-50, RCS2018-95, SR2018-34, ASN2018-31 
Volume (vol) vol.118 
Number (no) no.123(RCC), no.124(NS), no.125(RCS), no.126(SR), no.127(ASN) 
Page pp.75-76(RCC), pp.81-82(NS), pp.93-94(RCS), pp.85-86(SR), pp.91-92(ASN) 
#Pages 2 
Date of Issue 2018-07-04 (RCC, NS, RCS, SR, ASN) 

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