Composite Fading Models based on Inverse Gamma Shadowing: Theory and Validation

We introduce a general approach to characterize composite fading models based on inverse gamma (IG) shadowing. We first determine to what extent the IG distribution is an adequate choice for modeling shadow fading, by means of a comprehensive test with field measurements and other distributions conventionally used for this purpose. Then, we prove that the probability density function and cumulative distribution function of any IG-based composite fading model are directly expressed in terms of a Laplace-domain statistic of the underlying fast fading model and, in some relevant cases, as a mixture of wellknown state-of-the-art distributions. Also, exact and asymptotic expressions for the outage probability are provided, which are valid for any choice of baseline fading distribution. Finally, we exemplify our approach by presenting several application examples for IG-based composite fading models, for which their statistical characterization is directly obtained in a simple form.Comment: This work has been submitted to the IEEE for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Physical Layer Challenges and Solutions in Seamless Positioning via GNSS, Cellular and WLAN Systems

As different positioning applications have started to be a common part of our lives, positioning methods have to cope with increasing demands. Global Navigation Satellite System (GNSS) can offer accurate location estimate outdoors, but achieving seamless large-scale indoor localization remains still a challenging topic. The requirements for simple and cost-effective indoor positioning system have led to the utilization of wireless systems already available, such as cellular networks and Wireless Local Area Network (WLAN). One common approach with the advantage of a large-scale standard-independent implementation is based on the Received Signal Strength (RSS) measurements.This thesis addresses both GNSS and non-GNSS positioning algorithms and aims to offer a compact overview of the wireless localization issues, concentrating on some of the major challenges and solutions in GNSS and RSS-based positioning. The GNSS-related challenges addressed here refer to the channel modelling part for indoor GNSS and to the acquisition part in High Sensitivity (HS)-GNSS. The RSSrelated challenges addressed here refer to the data collection and calibration, channel effects such as path loss and shadowing, and three-dimensional indoor positioning estimation.This thesis presents a measurement-based analysis of indoor channel models for GNSS signals and of path loss and shadowing models for WLAN and cellular signals. Novel low-complexity acquisition algorithms are developed for HS-GNSS. In addition, a solution to transmitter topology evaluation and database reduction solutions for large-scale mobile-centric RSS-based positioning are proposed. This thesis also studies the effect of RSS offsets in the calibration phase and various floor estimators, and offers an extensive comparison of different RSS-based positioning algorithms