Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/16050
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dc.contributor.advisorAl-Raweshidy, H-
dc.contributor.advisorAbbod, M-
dc.contributor.authorKhan, Muhammad-
dc.date.accessioned2018-03-27T14:02:59Z-
dc.date.available2018-03-27T14:02:59Z-
dc.date.issued2018-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/16050-
dc.descriptionThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University Londonen_US
dc.description.abstractNetwork densification has become a dominant theme for capacity enhancement in cellular networks. However, it increases the operational complexity and expenditure for mobile network operators. Consequently, the essential features of Self-Organising Networks (SON) are considered to ensure the economic viability of the emerging cellular networks. This thesis focuses on quantifying the benefits of self-organisation in Cloud Radio Access Network (C-RAN) by proposing a flexible, energy efficient, and capacity optimised system. The Base Band Unit (BBU) and Remote Radio Head (RRH) map is formulated as an optimisation problem. A self-optimised C-RAN (SOCRAN) is proposed which hosts Genetic Algorithm (GA) and Discrete-Particle-Swarm-Optimisation algorithm (DPSO), developed for optimisation. Computational results based on different network scenarios demonstrate that DPSO delivers excellent performances for the key performance indicators compared to GA. The percentage of blocked users is reduced from 10.523% to 0.409% in a medium sized network scenario and 5.394% to 0.56% in a vast network scenario. Furthermore, an efficient resource utilisation scheme is proposed based on the concept of Cell Differentiation and Integration (CDI). The two-stage CDI scheme semi-statically scales the number of BBUs and RRHs to serve an offered load and dynamically defines the optimum BBU-RRH mapping to avoid unbalanced network scenarios. Computational results demonstrate significant throughput improvement in a CDI-enabled C-RAN compared to a fixed C-RAN, i.e., an average throughput increase of 45.53% and an average blocked users decrease of 23.149% is experienced. A power model is proposed to estimate the overall power consumption of C-RAN. Approximately 16% power reduction is calculated in a CDI-enabled C-RAN when compared to a fixed C-RAN, both serving the same geographical area. Moreover, a Divide-and-Sort load balancing scheme is proposed and compared to the SOCRAN scheme. Results show excellent performances by the Divide-and-Sort algorithm in small networks when compared to SOCRAN and K-mean clustering algorithm.en_US
dc.language.isoenen_US
dc.publisherBrunel University Londonen_US
dc.relation.urihttp://bura.brunel.ac.uk/bitstream/2438/16050/1/MuhammadKhan_PhDThesis.pdf-
dc.subjectCloud radio access network (C-RAN)en_US
dc.subjectSelf-organising networks (SON)en_US
dc.subjectArtificial intelligence (AI) techniques for load balancingen_US
dc.subjectDynamic resource allocationen_US
dc.subjectCell differentiation and integrationen_US
dc.titleA self-optimised cloud radio access network for emerging 5G architecturesen_US
dc.typeThesisen_US
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Electrical Engineering Theses

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