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Paper Abstract and Keywords
Presentation 2024-11-08 10:50
[Invited Lecture] Received Signal Strength Prediction using Multimodal Deep Learning
Khanh Nam Nguyen, Kenichi Takizawa (NICT) CS2024-65
Abstract (in Japanese) (See Japanese page) 
(in English) This study demonstrates the feasibility of predicting future 2.4 GHz Received Signal Strength Indicator (RSSI) for robot remote control applications using the integration of multiple types of data. The data includes visual information, including obtained camera images and Light Detection and Ranging (LiDAR) point clouds, and radio frequency (RF) data, which is obtained RSSI values. We propose an RSSI predictor that integrates and processes visual and RF data by fusing in a multimodal three-dimensional convolutional neural network. The prediction performance is evaluated using experimentally obtained images, LiDAR point clouds, and RSSI datasets from a mock-up area in a power plant environment. The results show that the proposed multimodal RSSI predictor outperforms single-modality approaches in terms of root mean square error, accuracy, correlation, and coefficient of determination.
Keyword (in Japanese) (See Japanese page) 
(in English) received signal strength prediction / multimodal learning / three-dimensional convolutional neural network / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 234, CS2024-65, pp. 77-81, Nov. 2024.
Paper # CS2024-65 
Date of Issue 2024-10-30 (CS) 
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 CS2024-65

Conference Information
Committee CS  
Conference Date 2024-11-06 - 2024-11-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka Public University I-site Namba C1 Conference Room 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Home networks, Sensory information networks, Quantum networks, Network service, CPS/Digital twin, Communication applications, etc. 
Paper Information
Registration To CS 
Conference Code 2024-11-CS 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Received Signal Strength Prediction using Multimodal Deep Learning 
Sub Title (in English)  
Keyword(1) received signal strength prediction  
Keyword(2) multimodal learning  
Keyword(3) three-dimensional convolutional neural network  
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1st Author's Name Khanh Nam Nguyen  
1st Author's Affiliation National Institute of Information and Communications Technology (NICT)
2nd Author's Name Kenichi Takizawa  
2nd Author's Affiliation National Institute of Information and Communications Technology (NICT)
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Speaker Author-1 
Date Time 2024-11-08 10:50:00 
Presentation Time 20 minutes 
Registration for CS 
Paper # CS2024-65 
Volume (vol) vol.124 
Number (no) no.234 
Page pp.77-81 
#Pages 5 
Date of Issue 2024-10-30 (CS) 

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