Handover optimization in GSM

The current trend in the cellular networks to reduce the amount of spectrum dedicated toGSM networks put a burden on the radio access part to keep the promised quality of service.In addition to the focus of operators on other radio access technologies and data networkshave raised the need to direct...

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Bibliographic Details
Main Author: Bazzari, Ahmad
Format: Others
Language:English
Published: KTH, Signalbehandling 2015
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175382
Description
Summary:The current trend in the cellular networks to reduce the amount of spectrum dedicated toGSM networks put a burden on the radio access part to keep the promised quality of service.In addition to the focus of operators on other radio access technologies and data networkshave raised the need to direct most of the effort towards other activities. Subsequently newtechniques are needed to compensate for the lack of time and resources to keep GSMnetworks as optimized as possible.The handover functionality is in the heart of any cellular system. It is more important whendelay is not tolerated. Therefore, the choice of its parameters is important. These parametersare chosen based on network performance and traffic load patterns among other criteria. It isa time consuming task, and investigating in a way to automatically choose appropriatesettings for its parameters is desired.This work investigates the possibility of such an automated procedure. A simulationenvironment is developed in order to run simulations. An automated function that utilizesthe principles of control theory, optimization, and live networks statistics is developed andused to build an algorithm to regulate the settings of the handover procedure.By regulating four parameters from the handover procedure, and by introducing differenttraffic loads, frequency reuse and mobility patterns, while testing the results against a set ofperformance metrics in the simulator. The results show better network performance in termsof performance metrics when the regulating algorithm is applied than when manuallychoosing values of these four parameters. However, further investigation shows that thealgorithm under an aggressive mobility pattern imitating a high speed moving users, mightneed a stopping rule. The algorithm tries to find a better network performance even if thecurrent metrics are acceptable. This appears to be invalid approach under some aggressivemodels.