Presentation 2018-05-17
Prediction Method by Deep-Learning using Convolutional Neural Network for Path Loss Characteristics in An Open-Square Environment
Nobuaki Kuno, Motoharu Sasaki, Yasushi Takatori,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) By using convolutional neural network which is widely used for recent image recognition etc., we propose a path loss prediction method in an open-square environment. By using ray-tracing data for learning, we verified whether the model can consider the influence of multiple blockages.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Deep-Learning / Convolutional Neural Network / Machine-Learning / Path Loss / Open-Square Environment
Paper # AP2018-21
Date of Issue 2018-05-10 (AP)

Conference Information
Committee AP
Conference Date 2018/5/17(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Kumamoto Univ.
Topics (in Japanese) (See Japanese page)
Topics (in English) Radio propagation, Antennas and Propagation
Chair Jiro Hirokawa(Tokyo Tech.)
Vice Chair Ryo Yamaguchi(SoftBank)
Secretary Ryo Yamaguchi(NTT DoCoMo)
Assistant Nobuyasu Takemura(Nippon Inst. of Tech.) / Satoshi Yamaguchi(Mitsubishi Electric)

Paper Information
Registration To Technical Committee on Antennas and Propagation
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Prediction Method by Deep-Learning using Convolutional Neural Network for Path Loss Characteristics in An Open-Square Environment
Sub Title (in English)
Keyword(1) Deep-Learning
Keyword(2) Convolutional Neural Network
Keyword(3) Machine-Learning
Keyword(4) Path Loss
Keyword(5) Open-Square Environment
1st Author's Name Nobuaki Kuno
1st Author's Affiliation Nippon Telegraph and Telephone Corporation(NTT)
2nd Author's Name Motoharu Sasaki
2nd Author's Affiliation Nippon Telegraph and Telephone Corporation(NTT)
3rd Author's Name Yasushi Takatori
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation(NTT)
Date 2018-05-17
Paper # AP2018-21
Volume (vol) vol.118
Number (no) AP-37
Page pp.pp.47-51(AP),
#Pages 5
Date of Issue 2018-05-10 (AP)