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Field strength prediction based on deep learning under small sample data
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  • MIN ZHOU,
  • Wei Shao,
  • Yang Liu,
  • Xiaoqin Yang
MIN ZHOU
Army Engineering University of PLA

Corresponding Author:[email protected]

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Wei Shao
Army Engineering University of PLA
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Yang Liu
Army Engineering University of PLA
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Xiaoqin Yang
Army Engineering University of PLA
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Abstract

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. Our 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, we construct a convolutional neural network (CNN) model 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.
05 Aug 2022Submitted to Electronics Letters
05 Aug 2022Submission Checks Completed
05 Aug 2022Assigned to Editor
10 Aug 2022Reviewer(s) Assigned
01 Sep 2022Review(s) Completed, Editorial Evaluation Pending
06 Sep 2022Editorial Decision: Revise Minor
09 Sep 20221st Revision Received
10 Sep 2022Submission Checks Completed
10 Sep 2022Assigned to Editor
10 Sep 2022Review(s) Completed, Editorial Evaluation Pending
13 Sep 2022Reviewer(s) Assigned
15 Sep 2022Editorial Decision: Accept
Nov 2022Published in Electronics Letters volume 58 issue 23 on pages 857-859. 10.1049/ell2.12631