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Paper Abstract and Keywords
Presentation 2019-01-24 15:45
On the Radio Environment Map Construction using Neural Network Residual Kriging
Koya Sato (TUS), Kei Inage (TMCIT), Takeo Fujii (UEC)
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we discuss the performance of feedforward neural network (FFNN) in radio environment map (REM) construction. We can realize a highly accurate REM via croudsourcing with Kriging. In the most works on the Kriging-aided REM construction, the measurement datasets are first regressed via linear regression in order to ensure spatial stationarity of the random variable. On the other hand, the path loss in the practical situation often contains an anisotropy due to effects of terrain and obstacles; thus, Kriging may not perform the optimal interpolation because of the regression error. In this paper, FFNN is used for the path loss modeling and the regression. Through theoretical, numerical and experimental discussions, we show situations where the FFNN can improve the accuracy of REM.
Keyword (in Japanese) (See Japanese page) 
(in English) machine learning / neural network / regression analysis / spatial interpolation / radio environment map / crowdsensing / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 421, SR2018-106, pp. 63-70, Jan. 2019.
Paper # SR2018-106 
Date of Issue 2019-01-17 (SR) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380

Conference Information
Committee SR  
Conference Date 2019-01-24 - 2019-01-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Corasse, Fukushima city (Fukushima prefecture) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) cognitive radio, machine learning application, heterogeneous network, SDN, IoT etc. 
Paper Information
Registration To SR 
Conference Code 2019-01-SR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) On the Radio Environment Map Construction using Neural Network Residual Kriging 
Sub Title (in English)  
Keyword(1) machine learning  
Keyword(2) neural network  
Keyword(3) regression analysis  
Keyword(4) spatial interpolation  
Keyword(5) radio environment map  
Keyword(6) crowdsensing  
1st Author's Name Koya Sato  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Kei Inage  
2nd Author's Affiliation Tokyo Metropolitan College of Industrial Technology (TMCIT)
3rd Author's Name Takeo Fujii  
3rd Author's Affiliation The University of Electro-Communications (UEC)
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Date Time 2019-01-24 15:45:00 
Presentation Time 25 
Registration for SR 
Paper # IEICE-SR2018-106 
Volume (vol) IEICE-118 
Number (no) no.421 
Page pp.63-70 
#Pages IEICE-8 
Date of Issue IEICE-SR-2019-01-17 

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