This master thesis investigates the possibility of locating LTE base stations, known as eNodeBs, using signal measurements collected by routers on trains. Four existing algorithms for transmitter localization are adopted: the centroid, strongest signal, Monte Carlo path loss simulation and power difference of arrival (PDoA) methods. An improved version of Monte Carlo path loss simulation called logloss fitting is proposed. Furthermore, a novel localization method called sector fitting is presented, which operates solely on the cell identity and geographical distribution of the measurements. The methods are evaluated for a set of manually located eNodeBs, and the results are compared to other external systems that can be used to locate eNodeBs. It is found that the novel sector fitting algorithm is able to considerably improve the accuracy of the logloss fitting and PDoA methods, but weighted centroid is overall the most accurate of the considered methods, providing a median error of approximately 1 km. The Google Geolocation API and Mozilla Location Service still provides estimates that are generally closer to the true location than any of the considered methods. However, for a subset of eNodeBs where measurements from all sectors are available, the novel sector fitting algorithm combined with logloss fitting outperforms the external systems. Therefore, a hybrid approach is suggested, where sector fitting combined with logloss fitting or weighted centroid is used to locate eNodeBs that have measurements from all sectors, while Google Geolocation API or Mozilla Location Service is used to locate the remaining eNodeBs. It is concluded that while the localization performance for those eNodeBs that have measurements from all sectors is relatively good, further improvements to the overall results can likely be obtained in future work by considering environmental factors, the angular losses introduced by directional antennas, and the effects of downlink power control.

In the current mobile network architecture, network traffic between user equipment (UE) and services deployed on the public cloud is tromboned towards the anchor point which could lead to network congestion. Deploying services closer to the UE, for example near the eNodeB, is a potential solution. The services are deployed on small scale data centers connected to, or collocated with the eNodeB, called ’eNodeB-Cloud’ (eNBC). Mobility of UEs presents a challenge for deploying services in an eNBC. When the UE is handed over from one eNodeB to another, seamless migration of UE context between the service instances running in different eNBCs needs to be ensured. In this thesis, we propose a Platform as a Service framework to enable UE context migration between eNBCs. The architecture consists of handover signaling mechanism, network session migration technology, context transfer protocol and a set of APIs towards the service. The evaluation of the prototype implementation shows that virtualization causes some extra delays to the UE context migration time. Whereas when virtualization is omitted, the time taken to migrate a UE context between two eNBCs is in the order of 12 ms on average, which is within the limit of handover interruption time between two LTE-eNodeBs.

The aim of this thesis is acquaintance with the EPS system of LTE and IMS subsystem. The thesis is aimed on analysis of the control procedures related to operations between the terminal and the packet core network of the fourth generation such as logging into the system, user authentication, change the viewing area, handover and disconnection from the network.

The aim of the work is the radio access network of mobile networks second, third and fourth generation. The main objective is a description of the radio access network of the second, third and fourth generation, their elements, interfaces and signaling procedures. The next part describes the emergency services and the architecture of the fourth generation mobile networks. The practical part is then devoted to identify actions needed to prepare radio access network to testing emergency services, identify actions needed to conversion of transport protocol fixed part of the radio access network from IPv4 to IPv6 and describe the intervention to radio access network to change the active segment of the core network.

Cellular network deployment scenarios refer to how cellular networks are implementedand deployed by network operators to provide wireless connectivity to end users.These scenarios can vary based on capacity requirements, type of geographical area, populationdensity, and specific use cases. Radio Access Networks of different generations,such as 4G and 5G, may also have different deployments. Network deployment scenarioscover many aspects, but two major components are Configuration settings and PerformanceMeasures which refer to the network nodes, hardware build-up and softwaresettings, and the end user behavior and connectivity experience in the area covered by thewireless network.In this master thesis, the aim is to understand how different area types - such as Rural,Suburban, and Urban – affect the cellular network deployment in such areas. A novelframework was developed to label each node (base station) with the area type it is associatedwith. The framework utilizes spatial analytics on the dataset provided by Ericsson forthe LTE nodes working with 4G technology in combination with open-source libraries anddatasets such as GeoPy and H3 Kontur population dataset respectively, to create area typelabels. The area types are labeled based on the calculated population density served byeach node and are considered true labels based on manual sanity checks performed. A supervisedmachine learning model was used to predict the nodes based on the CM and PMdata to understand the strength of the relationship between the features and true labels.This thesis also includes analysis and insights about characteristic deployment scenariosunder different area types. The main goal of this master thesis is to utilize machinelearning to uncover the characteristic features of a variety of node groups inherent in atelecom network, which, in the long run, contributes to better service operation and optimizationof existing cellular infrastructure. Nodes (base station) are labeled in the datato be able to distinguish their associated area-type. In addition to this clustering is performedto uncover the inherent characteristic behavior groups in the data and comparethem against the output from the classification model. Lastly, the investigation was doneon the potential impact of node placements such as indoor or outdoor, on the correspondingfeatures.In conclusion, the study’s results showed us that a correlation exists between deploymentscenarios and the different areas. There are a few prevalent common denominatorsbetween the node groups such as Pathloss and NR Cell Relations that drive the classificationmodel to a better classification metric, F1 score. Clustering of CM and PM data uncoversinherent patterns in different node groups under different area types and providesinformation about characteristic features of the groups such as CM data displaying twoconfiguration setting clusters, and PM data showing three different user behavior patterns.

With National Telecommunications & Information Administration (NTIA) Advanced Wireless Services (AWS-3) auction of frequencies in the 1695-1710 MHz, 1755-1780MHz, and 2155- 2180MHz bands, users of the Aeronautical Mobile Telemetry (AMT) band from 1755- 1850MHz, known as Upper L-Band, could be greatly affected. This paper takes an initial look at how the 1755-1780MHz band will be used by the cellular carriers and presents some preliminary testing results of adjacent channel (band) interference that could be experienced by AMT users. This paper should be considered as the stepping off point for future interference discussions, required analysis, and further testing.

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