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 |
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) |
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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) |
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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 |
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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 |
6 |
Date of Issue |
2023-07-13 (WBS, SRW, MICT) |
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