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Paper Abstract and Keywords
Presentation 2023-07-21 10:50
A Study on RSSI Prediction using LSTM Considering Inter-Channel Correlation in SmartBAN
Taito Toda, Takahiro Ito, Masaaki Yamanaka, Hirokazu Tanaka (Hiroshima City Univ.) WBS2023-21 SRW2023-19 MICT2023-18
Abstract (in Japanese) (See Japanese page) 
(in English) The demand for healthcare monitoring in daily life using wearable devices is increasing as a result of the shortage of doctors due to the aging population and the prophylaxis of lifestyle-related diseases. SmartBAN is one of the standards of Body Area Network (BAN) for medical healthcare IoT. For achieving highly-reliable communication, the migration algorithm from a bad condition channel to a good condition channel is required in order to avoid RF interferences from other devices. Since SmartBAN uses 2.4 GHz ISM bands, some devices using RF might be the interference sources, for example, wireless LAN, Bluetooth, microwave, etc. In this paper, we propose a method for predicting the RSSI of interferences considering inter-channel correlation in SmartBAN using long short-term memory (LSTM) network, which is one of the deep learning methods, and the performance of the proposed method is evaluated using RSSI measurement data.
Keyword (in Japanese) (See Japanese page) 
(in English) Body Area Network / SmartBAN / Long Short-Term Memory Network / Interference Avoidance / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 122, MICT2023-18, pp. 37-42, July 2023.
Paper # MICT2023-18 
Date of Issue 2023-07-13 (WBS, SRW, MICT) 
ISSN Online edition: ISSN 2432-6380
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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 WBS2023-21 SRW2023-19 MICT2023-18

Conference Information
Committee SRW MICT WBS  
Conference Date 2023-07-20 - 2023-07-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Niigata Univ. TOKIMATE 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network, MAC, WBAN, Sensor, mmWave, Medical and Healthcare ICT, Wideband (radio and optical) communications system, etc., Student poster session 
Paper Information
Registration To MICT 
Conference Code 2023-07-SRW-MICT-WBS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on RSSI Prediction using LSTM Considering Inter-Channel Correlation in SmartBAN 
Sub Title (in English)  
Keyword(1) Body Area Network  
Keyword(2) SmartBAN  
Keyword(3) Long Short-Term Memory Network  
Keyword(4) Interference Avoidance  
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1st Author's Name Taito Toda  
1st Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
2nd Author's Name Takahiro Ito  
2nd Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
3rd Author's Name Masaaki Yamanaka  
3rd Author's Affiliation Japan Coast Guard Academy (Hiroshima City Univ.)
4th Author's Name Hirokazu Tanaka  
4th Author's Affiliation Hiroshima City University (Hiroshima City Univ.)
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Speaker Author-1 
Date Time 2023-07-21 10:50:00 
Presentation Time 25 minutes 
Registration for MICT 
Paper # WBS2023-21, SRW2023-19, MICT2023-18 
Volume (vol) vol.123 
Number (no) no.120(WBS), no.121(SRW), no.122(MICT) 
Page pp.37-42 
#Pages
Date of Issue 2023-07-13 (WBS, SRW, MICT) 


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