A Geographic Information System Applied to Coverage Maps of 3G Cellular Communications Networks

Ponce-Rojas, Jazmín; Vidal-Beltrán, S.; Acevedo-Mosqueda, Marco A.; Jiménez-Licea, Montserrat

doi:10.4236/jgis.2011.32010

Cited by 3 publications

(2 citation statements)

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“…Among the reviewed scientific literature, there are many studies on signal strength applied for coverage analysis in cellular network communication. In the literature, many researchers have used geostatistical interpolation techniques, especially the Kriging method (Braham et al, 2017) and (Mezhoud et al, 2020) and(Ponce-Rojas et al, 2011), for coverage estimation. In studies in the literature, GIS has been used to visualize the propagation and coverage of signals and to perform spatial analyses in the planning of networks (Q.…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Received Signal Strength for Cellular Communication Using Support Vector Machine and K-Nearest Neighbours Regression

Perihanoglu,

Karaman

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Signal strength maps are of great importance for cellular system providers in network planning and operation. Accurate prediction of signal strength is important for solving problems such as link quality. In this study, Received Signal Strength (RSS) prediction model is proposed for the 900 MHz band in the Van Yüzüncü Yıl University campus environment by using machine learning regression methods such as K- Nearest Neıghbours (KNN) and Support Vector Regression (SVR) together with Geographic Information Systems. For the training of this model, signal strength values taken from the RF Spectrum Analyser at different locations and distances were used. In addition, spatial data sets such as the digital elevation model, location of base stations and measurement stations, building heights and location, and land use/cover were used in the model. The effect of these data sets on RSS power is included in the model. The model aims to predict RSS accurately, visualize the estimated signal strength, and analyze the signal field strength coverage. Different kernels from the SVR model such as Polynomial, , and Sigmoid were tested. To increase the success of the model, appropriate parameter values were selected and configured according to SVR and KNN methods. For 900 MHz, the performances of SVR and KNN models were compared and the results of the models were verified using root mean squares (RMSE). Among the measured data, the lowest prediction is found in KNN Manhattan. According to the results of the simulation was observed that the SVR model created with spatial data performs better for Signal Strength. Finally, the lowest RMSE value (1.71 dB) was obtained from the Sigmoid kernel in the best signal strength estimation SVR model. The SVR model is recommended for Campus Area signal strength estimation.

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Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Received Signal Strength for Cellular Communication Using Support Vector Machine and K-Nearest Neighbours Regression

Perihanoglu,

Karaman

2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…erefore, in this paper, we measure the coverage poverty experienced by the people. Several attempts have been done to calculate areas without coverage [8,9]. Effect of mobile coverage on railway network in Norway is studied in [10].…”

Section: Introductionmentioning

confidence: 99%

Mobile Coverage in Rural Sweden: Analysis of a Comparative Measurement Campaign

Sudheesh

Beek

2021

Mobile Information Systems

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Under the umbrella of 1G to 5G, different technologies have been used to provide mobile communication. Various technologies are being proposed to bring a person in remote area under coverage. However, a statistical analysis on what these users get from already existing technologies has not been carried out. We fill this gap by carrying out such a study using a measurement campaign, where we present a framework for analyzing mobile signal strength experienced at the user end. Measurements are taken throughout the Norrbotten county, the northernmost county in Sweden, using mobile phones recording various parameters at regular intervals. Based on measured signal strength, a coverage map has been made via inverse distance weighting (IDW) interpolation. Based on the coverage map, various analyses are carried out on signal strength over residential areas and roads of Norrbotten. Overall, we lay a framework to analyze and quantify the effect of signal strength on users.

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A Comparison between Theoretical and Experimental Coverage Analysis in 3G Cellular Networks

Ponce-Rojas¹,

Eslava-Gutierrez²,

Jiménez-Licea³

et al. 2012

IJCNS

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This paper shows a comparison between theoretical and experimental coverage analysis. Theoretical work is based on Okumura-Hata propagation model, which are compared with the measured data obtained through an experimental analysis en México City. It is important because all the network designers and managers have to take in account how the signals will arrive to the mobile devices. If they know it, they can install Nodes B (base stations) in the better place in the area in order to take advantage of the power radiated by the antenna.

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A Geographic Information System Applied to Coverage Maps of 3G Cellular Communications Networks

Cited by 3 publications

References 1 publication

Spatial Prediction of Received Signal Strength for Cellular Communication Using Support Vector Machine and K-Nearest Neighbours Regression

Spatial Prediction of Received Signal Strength for Cellular Communication Using Support Vector Machine and K-Nearest Neighbours Regression

Mobile Coverage in Rural Sweden: Analysis of a Comparative Measurement Campaign

A Comparison between Theoretical and Experimental Coverage Analysis in 3G Cellular Networks

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