Kampüs Ortaminda Yol Kaybi Tahmini için Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yaklaşim

Guzide Miray Perihanoǧlu; Himmet Karaman

doi:10.1109/SIU59756.2023.10223770

Kampüs Ortaminda Yol Kaybi Tahmini için Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yaklaşim

Translated title of the contribution: Geographic Information Systems (GIS) and Random Forests Regression-Based Approach for Path Loss Prediction in the Campus Environment

Guzide Miray Perihanoǧlu, Himmet Karaman

Department of Geomatics Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Estimation of path loss in communication systems is a fundamental requirement for designing any mobile or wireless communication system and assessing the quality of service. In this study, Random Forest machine regression algorithm and GIS based path loss estimation method for 1800 and 2100 MHz in Van Yüzüncü Yil University campus area are proposed. The goal of this model is to predict the coverage area, visualize the predicted signal path loss and analyze the field strength coverage. In order to increase the training efficiency of the model, the selected parameter values are structured according to the performance metrics. Based on the preliminary information obtained from the measured results, a series of training sets were created. According to the experimental results, it was observed that the Random Forest and GIS-based model performed effectively in different terrain classes for path loss estimation.

Translated title of the contribution	Geographic Information Systems (GIS) and Random Forests Regression-Based Approach for Path Loss Prediction in the Campus Environment
Original language	Turkish
Title of host publication	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350343557
DOIs	https://doi.org/10.1109/SIU59756.2023.10223770
Publication status	Published - 2023
Event	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Turkey Duration: 5 Jul 2023 → 8 Jul 2023

Publication series

Name	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Conference

Conference	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Country/Territory	Turkey
City	Istanbul
Period	5/07/23 → 8/07/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Access to Document

10.1109/SIU59756.2023.10223770

Cite this

Perihanoǧlu, G. M., & Karaman, H. (2023). Kampüs Ortaminda Yol Kaybi Tahmini için Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yaklaşim. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU59756.2023.10223770

Perihanoǧlu, Guzide Miray ; Karaman, Himmet. / Kampüs Ortaminda Yol Kaybi Tahmini için Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yaklaşim. 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023).

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title = "Kamp{\"u}s Ortaminda Yol Kaybi Tahmini i{\c c}in Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yakla{\c s}im",

abstract = "Estimation of path loss in communication systems is a fundamental requirement for designing any mobile or wireless communication system and assessing the quality of service. In this study, Random Forest machine regression algorithm and GIS based path loss estimation method for 1800 and 2100 MHz in Van Y{\"u}z{\"u}nc{\"u} Yil University campus area are proposed. The goal of this model is to predict the coverage area, visualize the predicted signal path loss and analyze the field strength coverage. In order to increase the training efficiency of the model, the selected parameter values are structured according to the performance metrics. Based on the preliminary information obtained from the measured results, a series of training sets were created. According to the experimental results, it was observed that the Random Forest and GIS-based model performed effectively in different terrain classes for path loss estimation.",

keywords = "Geographic information systems(GIS), Path loss model, Random forests regression",

author = "Perihanoǧlu, {Guzide Miray} and Himmet Karaman",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 ; Conference date: 05-07-2023 Through 08-07-2023",

year = "2023",

doi = "10.1109/SIU59756.2023.10223770",

language = "T{\"u}rk{\c c}e",

series = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

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Perihanoǧlu, GM & Karaman, H 2023, Kampüs Ortaminda Yol Kaybi Tahmini için Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yaklaşim. in 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Institute of Electrical and Electronics Engineers Inc., 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Istanbul, Turkey, 5/07/23. https://doi.org/10.1109/SIU59756.2023.10223770

Kampüs Ortaminda Yol Kaybi Tahmini için Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yaklaşim. / Perihanoǧlu, Guzide Miray; Karaman, Himmet.
31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Kampüs Ortaminda Yol Kaybi Tahmini için Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yaklaşim

AU - Perihanoǧlu, Guzide Miray

AU - Karaman, Himmet

PY - 2023

Y1 - 2023

N2 - Estimation of path loss in communication systems is a fundamental requirement for designing any mobile or wireless communication system and assessing the quality of service. In this study, Random Forest machine regression algorithm and GIS based path loss estimation method for 1800 and 2100 MHz in Van Yüzüncü Yil University campus area are proposed. The goal of this model is to predict the coverage area, visualize the predicted signal path loss and analyze the field strength coverage. In order to increase the training efficiency of the model, the selected parameter values are structured according to the performance metrics. Based on the preliminary information obtained from the measured results, a series of training sets were created. According to the experimental results, it was observed that the Random Forest and GIS-based model performed effectively in different terrain classes for path loss estimation.

AB - Estimation of path loss in communication systems is a fundamental requirement for designing any mobile or wireless communication system and assessing the quality of service. In this study, Random Forest machine regression algorithm and GIS based path loss estimation method for 1800 and 2100 MHz in Van Yüzüncü Yil University campus area are proposed. The goal of this model is to predict the coverage area, visualize the predicted signal path loss and analyze the field strength coverage. In order to increase the training efficiency of the model, the selected parameter values are structured according to the performance metrics. Based on the preliminary information obtained from the measured results, a series of training sets were created. According to the experimental results, it was observed that the Random Forest and GIS-based model performed effectively in different terrain classes for path loss estimation.

KW - Geographic information systems(GIS)

KW - Path loss model

KW - Random forests regression

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U2 - 10.1109/SIU59756.2023.10223770

DO - 10.1109/SIU59756.2023.10223770

M3 - Konferans katkısı

AN - SCOPUS:85173430160

T3 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

BT - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Y2 - 5 July 2023 through 8 July 2023

ER -

Perihanoǧlu GM, Karaman H. Kampüs Ortaminda Yol Kaybi Tahmini için Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yaklaşim. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). doi: 10.1109/SIU59756.2023.10223770

Kampüs Ortaminda Yol Kaybi Tahmini için Coǧrafi Bilgi Sistemleri (CBS) ve Rastgele Orman Regresyonu Tabanli Yaklaşim

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