Operational mobile service quality in trains and road vehicles

Abstract

In the past, railway vehicles have been exclusively designed as a land transportation system to serve the society in motion. Later, new generation of low-emissivity windows became integral part of vehicle windows with purpose to ensure comfort and enhance safety of passengers on-board. Nowadays, trains are pivotal elements in railway environments used for commuting people as well as in freight transport. However, low-emissivity coating, multiple window panes, isolating materials deposited between panes as well as metallic materials used to construct the vehicle body itself pose a big impediment to wireless communications. Yet, the problem remains for railway environments, while growing demands for seamless connectivity are not being satisfied from existing solutions owing to high values of Vehicle Penetration Loss and sparse infrastructure deployments. On the other hand, even though dedicated (trackside) cellular networks potentially address the problem of high Vehicle Penetration Loss, poor coverage areas, abrupt changes in signal strengths, high number of handovers, unstable data rates, or high number of retransmissions, yet, they are considered very costly from cellular operators. While mobile users are on move, they frequently interact to their communication devices, such as smartphones or laptops. Therefore, this dissertation motivates real-world smartphone-based measurement campaigns carried out along large-scale railway tracks with purpose to mimic the quasi-real usage of end-users. Particularly, it investigates RF behaviors of both active and passive architectures that improve the performance of mobile users on-board railway vehicles. However, precise benefits of commercially available solutions on smartphones are unknown in literature. On one hand, key performance indicators are employed to analyze the performance of Universal Mobile Telecommunications System (UMTS)/Long Term Evolution (LTE) mobile users operating at wireless frequencies 8002600 MHz, while on the other hand, operational network and measurement limitations challenge the statistical evaluation and as such they require robust statistical approaches. In general, nonparametric statistical inference and descriptive statistics are applied to make predictions on improvements or impairments for various vehicular use cases. In particular, multiple hypothesis testing is constructed upon rank-based methods which apply to the two-sample problem to infer changes about central tendency. The scope of this dissertation includes the use cases of mobile users on-board inter-city and regional trains equipped with wideband Amplify-and-Forward Repeaters and Frequency Selective Surfaces as well as mobile users on-board road vehicles investigated from the perspective of infotainment applications, voice calls and packet data transfers.