Highly accurate prediction of radio propagation using model classifier

Keita Katagiri, Keita Onose, Koya Sato, Kei Inage, Takeo Fujii

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we propose a measurement-based spectrum database using model classifier. In the radio propagation, path loss is the fundamental factor to recognize the coverage area. However, it is difficult to accurately estimate the radio environment only path loss estimation because of the shadowing deviation. Therefore, we estimate the propagation model including shadowing, and by determining the usage range of the estimated model, we reduce the registered data size while accurately estimating the radio environment. The database firstly accumulates the received signal strength indicator (RSSI) related to the locations of receivers and we construct the model classifier. Then, the database assigns the propagation model in each mesh so that Root Mean Squared Error (RMSE) between datasets and the models is minimized. We used measurement datasets of a 3GPP cellular band in the real environment to construct the model classifier. Our results show that the proposed method can accurately estimate the radio propagation while the registered data size is significantly reduced. Additionally, we discuss a method of power control based on the proposed method for improving the communication efficiency.

Original languageEnglish
Title of host publication2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728112176
DOIs
Publication statusPublished - Apr 2019
Event89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia
Duration: 28 Apr 20191 May 2019

Publication series

NameIEEE Vehicular Technology Conference
Volume2019-April
ISSN (Print)1550-2252

Conference

Conference89th IEEE Vehicular Technology Conference, VTC Spring 2019
CountryMalaysia
CityKuala Lumpur
Period28/04/191/05/19

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  • Cite this

    Katagiri, K., Onose, K., Sato, K., Inage, K., & Fujii, T. (2019). Highly accurate prediction of radio propagation using model classifier. In 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings [8746323] (IEEE Vehicular Technology Conference; Vol. 2019-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCSpring.2019.8746323