Open AccessField strength prediction based on deep learning under small sample data
Author(s) -
Zhou Min,
Shao Wei,
Liu Yang,
Yang Xiaoqin
Publication year - 2022
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12631
Subject(s) - computer science , field (mathematics) , deep learning , convolutional neural network , artificial intelligence , artificial neural network , sample (material) , matching (statistics) , machine learning , data mining , data modeling , pattern recognition (psychology) , mathematics , chemistry , statistics , chromatography , database , pure mathematics
The accurate prediction of radio wave propagation is extremely important for wireless network planning and optimization. However, inexact matching between the traditional empirical model and actual propagation environments, as well as the insufficiency of the sample data required for training a deep learning model, lead to unsatisfactory prediction results. This paper proposes a field strength prediction model based on a deep neural network that is aimed at a tiny dataset composed of the geographic information and corresponding satellite images of a target area. This model connects two pretrained networks to minimize the parameters to be learned. Simultaneously, a convolutional neural network (CNN) model is constructed for comparison based on a previous advanced study in this field. Experimental results show that the proposed model can obtain the same accuracy as that of previously developed CNN models while requiring less data.