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Paper Abstract and Keywords
Presentation 2020-12-18 15:50
K-Factor Estimation based on Spectrogram Images by Convolutional Neural Network
Shun Kojima, Kosuke Shima (Chiba Univ.), Kazuki Maruta (Tokyo Tech), Chang-Jun Ahn (Chiba Univ.) RCS2020-153
Abstract (in Japanese) (See Japanese page) 
(in English) In the next generation mobile communications systems, accurate and fast acquisition of the communication environment is essential to achieve high-speed and low-latency communication. A $K$-factor in Rician fading is a major determinant of the link quality. Therefore, $K$-factor estimation is a very important task in order to achieve high performance of adaptive control in wireless communications that is dependent on the link quality. Conventional methods for estimating the $K$-factor have been widely studied, including the method using moments of the received signal and the method based on the received signal envelope and channel statistics. These methods require sampling of the signal on a large scale, which increases the amount of computation and processing time. In this paper, we focus on spectrograms containing features of the $K$-factor and propose a novel its estimation method using convolutional neural network (CNN) from a single packet. Simulation results reveal its effectiveness in terms of estimation accuracy and resultant adaptive modulation performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Spectrogram / CNN / K factor estimation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 298, RCS2020-153, pp. 103-108, Dec. 2020.
Paper # RCS2020-153 
Date of Issue 2020-12-10 (RCS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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)
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Conference Information
Committee NS RCS  
Conference Date 2020-12-17 - 2020-12-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Multi-hop/Relay/Cooperation, Disaster-resistant wireless network, Sensor/Mesh network, Ad-hoc network, D2D/M2M, Wireless network coding, Handover/AP switching/Connected cell control/Load balancing among base stations/Mobile network dynamic reconfiguration, QoS/QoE assurance, Wireless VoIP, IoT, Edge computing, etc. 
Paper Information
Registration To RCS 
Conference Code 2020-12-NS-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) K-Factor Estimation based on Spectrogram Images by Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) Spectrogram  
Keyword(2) CNN  
Keyword(3) K factor estimation  
1st Author's Name Shun Kojima  
1st Author's Affiliation Chiba University (Chiba Univ.)
2nd Author's Name Kosuke Shima  
2nd Author's Affiliation Chiba University (Chiba Univ.)
3rd Author's Name Kazuki Maruta  
3rd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
4th Author's Name Chang-Jun Ahn  
4th Author's Affiliation Chiba University (Chiba Univ.)
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Date Time 2020-12-18 15:50:00 
Presentation Time 25 
Registration for RCS 
Paper # IEICE-RCS2020-153 
Volume (vol) IEICE-120 
Number (no) no.298 
Page pp.103-108 
#Pages IEICE-6 
Date of Issue IEICE-RCS-2020-12-10 

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