Learning-Based Path Loss Estimation Using Multiple Spatial Data and System Parameters

We propose a novel path loss estimation method based on deep learning with some newly defined system parameters and images. Estimating the radio wave propagation environment is one of the key techniques for indoor/outdoor high-speed wireless communication. The radio wave propagation environment is b...

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Bibliographic Details
Published in2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) pp. 1 - 5
Main Authors Inoue, Kazuya, Imaizumi, Keita, Ichige, Koichi, Nagao, Tatsuya, Hayashi, Takahiro
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2022
Subjects
Deep learning
Estimation
Propagation
Propagation losses
Spatial databases
Vehicular and wireless technologies
Wireless communication
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Summary:We propose a novel path loss estimation method based on deep learning with some newly defined system parameters and images. Estimating the radio wave propagation environment is one of the key techniques for indoor/outdoor high-speed wireless communication. The radio wave propagation environment is basically a multipath environment, and path loss characteristics should be estimated under various environments. The authors have already proposed path loss estimation methods based on machine learning and spatial image data. The purpose of this paper is to further enhance the path loss estimation accuracy by appropriately selecting the input parameters and the CNN/FNN model structure.
ISSN:2577-2465
DOI:10.1109/VTC2022-Fall57202.2022.10012870