UMTS network optimisation

Oliver, Kathryn Elizabeth 2005. UMTS network optimisation. PhD Thesis, Cardiff University.

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Abstract

Network operators desire effective, pragmatic solutions to instances of the cell planning problem in order to improve their quality of service, enhance network coverage and capacity capability, and ultimately increase company profits. Previ ous cell plans have been constructed manually but these methods do not produce the best network configuration. More reliance has since been placed on automated cell planning to produce effective solutions. The introduction of the Universal Mobile Telecommunication System (UMTS) emphasizes the need for high perfor mance planning tools. Motivated by a discussion of the literature concerning cell planning, an existing model for Global System for Mobile Communication (GSM) is modified to take account of the requirements of UMTS networks. A suite of test cases is created using a purpose-built problem generator, including problems with a range of site and traffic distributions for rural, suburban and urban markets. Traditionally, cell planning has been seen purely as an optimisation problem, neglecting the pre-optimisation stage of network dimensioning. This thesis inves tigates the effect of network dimensioning as a precursor to optimisation demon strating the benefits of cell planning in three stages consisting of site estimation, site selection and optimisation. The first stage, site estimation, utilises previously published lower bounding techniques to provide a means of approximating the number of sites required to meet capacity targets in the uplink and downlink. Site selection compares random selection to three newly developed algorithms to make effective automatic selections of sites from a candidate set. The final optimiza tion phase presents a framework based on the tabu search meta-heuristic capable of optimising the dimensioned network designs with respect to the representative operational scenarios. Multiple traffic snapshot evaluations are considered in the optimisation objective function.

Item Type:	Thesis (PhD)
Status:	Unpublished
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISBN:	9781303204326
Date of First Compliant Deposit:	30 March 2016
Last Modified:	12 Feb 2016 23:12
URI:	https://orca.cardiff.ac.uk/id/eprint/54561

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