Optimierung der Handover Entscheidung in Infrastrukturnetzen unter Verwendung von realistischen Simulationsumgebungen

The development of mobile communication services and technologies in recent years boosts the importance and ubiquity of terminal equipments in our everyday life. The main drivers for this development are the reliability of the offered services and the user friendliness, allowing a huge variety of communication services with a single device. To assure a high communication quality and the usability of the services a seamless connectivity is beneficial or even mandatory, e.g. for voice calls, video streaming, gaming or safety-critical application based on car-to-car communication. Due to the cellular nature of infrastructure networks, mobile users will cross cell boundaries and need to switch the serving cell with the help of a handover procedure. The timing of the handover is essential to keep the mobile devices connected to the network. The introduction of measurement based optimisation in the context of self-organising networks enables the optimisation of the handover decision. The key enabler for the optimisation are a cost function that incorporates the relevant handover performance indicators, a reasonable observation time to evaluate the performance and an optimisation algorithm that reliably improves the handover performance in various, ever-changing network conditions. In the recent years several handover optimisation algorithms have been investigated. Nevertheless, the influence of the target function on the optimisation, the dimensioning of the observation window and the impact of network condition changes have not been investigated so far. In this dissertation a detailed analysis of the handover performance indicators is presented. Beyond that, additional system information or measurements are valued as potential candidates to allow further improvement of the handover performance. Particular attention is paid to the ability to adapt to changing network conditions since the introduction of new cell layers (small cells), new techniques like adaptive antenna systems or spectrum sharing or the introduction of new communication technologies like LTE-Advanced increases the complexity of future mobile communication networks. Finally, we develop an optimisation algorithm that reliably and quickly optimises the handover performance in various and fast-changing network conditions.Mobile Endgeräte gewinnen in unserem täglichen Leben zunehmend an Bedeutung. Dieser Trend wird vorangetrieben durch die rasante Entwicklung der Mobilfunktechnologien und neu angebotene Dienste in den letzten Jahren. Immer mehr Dienstleistungen werden über ein einzelnes Endgerät bereitgestellt. Um eine hohe Übertragungsqualität zur Nutzung der Dienste sicherzustellen, ist eine nahtlose Verbindung zum Kommunikationsnetzwerk wünschenswert oder sogar obligatorisch, z.B. für Sprachverbindungen, Video-Streaming, Onlinespiele oder sicherheitsrelevante Anwendungen der Car-to-Car-Kommunikation. Bedingt durch die zellulare Struktur der Mobilfunknetze ist zur Aufrechterhaltung der Kommunikation ein Zellwechsel (Handover) im Randbereich des Versorgungsgebietes einer Zelle notwendig. Der genaue Zeitpunkt des Zellwechsels ist dabei von besonderer Bedeutung. Die Einführung der messungsbasierten Selbst-Optimierung für Mobilfunknetze ermöglicht die Optimierung der Zellwechsel-Entscheidung. Die wesentlichen Voraussetzungen für eine Optimierung sind eine Optimierungszielfunktion auf Basis der Leistungsindikatoren, eine angemessene Beobachtungszeit sowie die Entwicklung eines möglichst allgemeingültigen Optimierungsverfahrens. In den letzten Jahren sind viele solcher Verfahren untersucht und veröffentlicht worden. Dennoch sind der Einfluss der Zielfunktion auf die Optimierung, die Dimensionierung des Beobachtungszeitraums und die Auswirkungen von Netzzustandsänderungen auf die Optimierung bisher weitgehend vernachlässigt worden. In dieser Arbeit wird eine detaillierte Analyse der Zellwechsel-Leistungsindikatoren in LTE durchgeführt. Darüber hinaus wird die Eignung zusätzlicher Systeminformationen oder Messungen zur weiteren Verbesserung der Zellwechsel-Entscheidung untersucht. Durch die Einführung neuer Zelltypen (z.B. Small Cells), moderner Übertragungstechniken wie adaptive Antennensysteme oder die Einführung neuer Technologien wie LTE Advanced nimmt die Komplexität der zukünftigen Mobilfunknetze stetig zu. Das in dieser Arbeit entwickelte Optimierungsverfahren ermöglicht eine schnelle und zuverlässige Anpassung der Zellwechselparameter an die veränderlichen Bedingungen in den Mobilfunknetzen und kann daher auch in komplexeren Systemen eingesetzt werden

A case study: Failure prediction in a real LTE network

Mobile traffic and number of connected devices have been increasing exponentially nowadays, with customer expectation from mobile operators in term of quality and reliability is higher and higher. This places pressure on operators to invest as well as to operate their growing infrastructures. As such, telecom network management becomes an essential problem. To reduce cost and maintain network performance, operators need to bring more automation and intelligence into their management system. Self-Organizing Networks function (SON) is an automation technology aiming to maximize performance in mobility networks by bringing autonomous adaptability and reducing human intervention in network management and operations. Three main areas of SON include self-configuration (auto-configuration when new element enter the network), self-optimization (optimization of the network parameters during operation) and self-healing (maintenance). The main purpose of the thesis is to illustrate how anomaly detection methods can be applied to SON functions, in particularly self-healing functions such as fault detection and cell outage management. The thesis is illustrated by a case study, in which the anomalies - in this case, the failure alarms, are predicted in advance using performance measurement data (PM data) collected from a real LTE network within a certain timeframe. Failures prediction or anomalies detection can help reduce cost and maintenance time in mobile network base stations. The author aims to answer the research questions: what anomaly detection models could detect the anomalies in advance, and what type of anomalies can be well-detected using those models. Using cross-validation, the thesis shows that random forest method is the best performing model out of the chosen ones, with F1-score of 0.58, 0.96 and 0.52 for the anomalies: Failure in Optical Interface, Temperature alarm, and VSWR minor alarm respectively. Those are also the anomalies can be well-detected by the model

Developing Travel Behaviour Models Using Mobile Phone Data

Improving the performance and efficiency of transport systems requires sound decision-making supported by data and models. However, conducting travel surveys to facilitate travel behaviour model estimation is an expensive venture. Hence, such surveys are typically infrequent in nature, and cover limited sample sizes. Furthermore, the quality of such data is often affected by reporting errors and changes in the respondents’ behaviour due to awareness of being observed. On the other hand, large and diverse quantities of time-stamped location data are nowadays passively generated as a by-product of technological growth. These passive data sources include Global Positioning System (GPS) traces, mobile phone network records, smart card data and social media data, to name but a few. Among these, mobile phone network records (i.e. call detail records (CDRs) and Global Systems for Mobile Communication (GSM) data) offer the biggest promise due to the increasing mobile phone penetration rates in both the developed and the developing worlds. Previous studies using mobile phone data have primarily focused on extracting travel patterns and trends rather than establishing mathematical relationships between the observed behaviour and the causal factors to predict the travel behaviour in alternative policy scenarios. This research aims to extend the application of mobile phone data to travel behaviour modelling and policy analysis by augmenting the data with information derived from other sources. This comes along with significant challenges stemming from the anonymous and noisy nature of the data. Consequently, novel data fusion and modelling frameworks have been developed and tested for different modelling scenarios to demonstrate the potential of this emerging low-cost data source. In the context of trip generation, a hybrid modelling framework has been developed to account for the anonymous nature of CDR data. This involves fusing the CDR and demographic data of a sub-sample of the users to estimate a demographic prediction sub-model based on phone usage variables extracted from the data. The demographic group membership probabilities from this model are then used as class weights in a latent class model for trip generation based on trip rates extracted from the GSM data of the same users. Once estimated, the hybrid model can be applied to probabilistically infer the socio-demographics, and subsequently, the trip generation of a large proportion of the population where only large-scale anonymous CDR data is available as an input. The estimation and validation results using data from Switzerland show that the hybrid model competes well against a typical trip generation model estimated using data with known socio-demographics of the users. The hybrid framework can be applied to other travel behaviour modelling contexts using CDR data (in mode or route choice for instance). The potential of CDR data to capture rational route choice behaviour for long-distance inter-regional O-D pairs (joined by highly overlapping routes) is demonstrated through data fusion with information on the attributes of the alternatives extracted from multiple external sources. The effect of location discontinuities in CDR data (due to its event-driven nature), and how this impacts the ability to observe the users’ trajectories in a highly overlapping network is discussed prompting the development of a route identification algorithm that distinguishes between unique and broad sub-group route choices. The broad choice framework, which was developed in the context of vehicle type choice is then adapted to leverage this limitation where unique route choices cannot be observed for some users, and only the broad sub-groups of the possible overlapping routes are identifiable. The estimation and validation results using data from Senegal show that CDR data can capture rational route choice behaviour, as well as reasonable value of travel time estimates. Still relying on data fusion, a novel method based on the mixed logit framework is developed to enable the analysis of departure time choice behaviour using passively collected data (GSM and GPS data) where the challenge is to deal with the lack of information on the desired times of travel. The proposed method relies on data fusion with travel time information extracted from Google Maps in the context of Switzerland. It is unique in the sense that it allows the modeller to understand the sensitivity attached to schedule delay, thus enabling its valuation, despite the passive nature of the data. The model results are in line with the expected travel behaviour, and the schedule delay valuation estimates are reasonable for the study area. Finally, a joint trip generation modelling framework fusing CDR, household travel survey, and census data is developed. The framework adjusts the scaling factors of a traditional trip generation model (based on household travel survey data only) to optimise model performance at both the disaggregate and aggregate levels. The framework is calibrated using data from Bangladesh and the adjusted models are found to have better spatial and temporal transferability. Thus, besides demonstrating the potential of mobile phone data, the thesis makes significant methodological and applied contributions. The use of different datasets provides rich insights that can inform policy measures related to the adoption of big data for transport studies. The research findings are particularly timely for transport agencies and practitioners working in contexts with severe data limitations (especially in developing countries), as well as academics generally interested in exploring the potential of emerging big data sources, both in transport and beyond

Prediction assisted fast handovers for seamless IP mobility

Word processed copy.Includes bibliographical references (leaves 94-98).This research investigates the techniques used to improve the standard Mobile IP handover process and provide proactivity in network mobility management. Numerous fast handover proposals in the literature have recently adopted a cross-layer approach to enhance movement detection functionality and make terminal mobility more seamless. Such fast handover protocols are dependent on an anticipated link-layer trigger or pre-trigger to perform pre-handover service establishment operations. This research identifies the practical difficulties involved in implementing this type of trigger and proposes an alternative solution that integrates the concept of mobility prediction into a reactive fast handover scheme