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Paper Abstract and Keywords
Presentation 2021-11-10 15:55
Channel Parameter Estimation by using Environmental Features
Inocent Calist, Zhiqiang Li, Minseok Kim (Niigata Univ.) AP2021-106
Abstract (in Japanese) (See Japanese page) 
(in English) Recent developments in the next generation of mobile communication and the application of the Internet of things has raised the need to develop more accurate channel models. This work presents the development of a supervised based machine learning (ML) prediction model for large scale channel parameters (LSCPs) estimation by analyzing the reflected multipath ray's information. The reflected rays varies with the morphology structure of the propagation environment, hence a dynamic LSCPs predictive model can be realized. The input parameters to the prediction model are transmitter (TX) and receiver (RX) positional coordinates, and the reflected rays' information such as the delay, angle of arrival, angle of departure, elevation angle of arrival, elevation angle of departure, and power gain. The proposed model was implemented using Random Forest (RF) which can predict both linear and nonlinear data. Ray tracing (RT) simulation was performed to calculate the input measurement dataset of the LSCPs, and the input information of the reflected rays. Cross validation was then utilized to validate the model.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / parameter estimation / channel / rays information / prediction model / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 233, AP2021-106, pp. 34-38, Nov. 2021.
Paper # AP2021-106 
Date of Issue 2021-11-03 (AP) 
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 AP2021-106

Conference Information
Committee AP RCS  
Conference Date 2021-11-10 - 2021-11-12 
Place (in Japanese) (See Japanese page) 
Place (in English) NBC-Bekkan (Nagasaki) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Adaptive Antenna, Equalization, Interference Canceler, MIMO, Wireless Communications, etc. 
Paper Information
Registration To AP 
Conference Code 2021-11-AP-RCS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Channel Parameter Estimation by using Environmental Features 
Sub Title (in English)  
Keyword(1) Machine learning  
Keyword(2) parameter estimation  
Keyword(3) channel  
Keyword(4) rays information  
Keyword(5) prediction model  
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1st Author's Name Inocent Calist  
1st Author's Affiliation Niigata University (Niigata Univ.)
2nd Author's Name Zhiqiang Li  
2nd Author's Affiliation Niigata University (Niigata Univ.)
3rd Author's Name Minseok Kim  
3rd Author's Affiliation Niigata University (Niigata Univ.)
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Speaker Author-1 
Date Time 2021-11-10 15:55:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2021-106 
Volume (vol) vol.121 
Number (no) no.233 
Page pp.34-38 
#Pages 5 
Date of Issue 2021-11-03 (AP) 

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