Urban, suburban and rural channel models based on cellular and wireless area network signals for positioning purposes
Beitia, Jon Ander
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Location information is becoming more and more useful in everyday life. The cellular- based and wireless area network based positioning are being used to predict the location of users in environments such as indoors and densely urban, where the Global Navigation Satellite Systems often fail. The Received Signal Strength (RSS) is used more and more due to its wide availability on any mobile device and based on various signals. However, studies about the availability of signal measurements in various urban and sub-urban scenarios and about the RSS fluctuations in various terrain types are still lacking in the current literature. The main objective of this thesis is to analyze method wide range of measurement-based RSS in urban, suburban and rural environments. The thesis is divided in two parts. In the literature study part, issues related to cellular and wireless local area networks (WLANs) such as frequency bands, system architectures, radio interfaces and RSS measurements are described. Also the propagation channel characteristics and the different path-loss models existing in the literature are exposed. The information explained in this part was useful in order to be able to process the available data measurements. In the measurements and analysis part, the collected results were evaluated. The different parameters of cellular and WLAN signals were studied and compared. Availability of signals in various scenarios, density of the transmitters and path loss coefficients were deeply analyzed, by dividing the measured scenarios into seven distinct environments: airport areas, Seaside or beach areas, Mountain or forest areas, suburban areas, densely urban areas, lake areas, and other urban areas. The obtained results provide more insight into the availability of RSS signal measurements on mobile devices nowadays and into the possibility of creating generic and unified RSS-based positioning algorithms (based on the similarities between different types of signals and different types of environments).