Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/14804
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dc.contributor.authorAl-Raweshidy, H-
dc.contributor.authorKhan, M-
dc.contributor.authorAlhumaima, R-
dc.date.accessioned2017-06-21T13:20:59Z-
dc.date.available2017-10-25-
dc.date.available2017-06-21T13:20:59Z-
dc.date.issued2017-
dc.identifier.citationIEEE Transactions on Network and Service Management, 2017en_US
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/14804-
dc.description.abstractAn inefficient utilisation of network resources in a time-varying traffic environment often leads to load imbalances, high call-blocking events and degraded Quality of Service (QoS). This paper optimises the QoS of a Cloud Radio Access Network (C-RAN) by investigating load balancing solutions. The dynamic re-mapping ability of C-RAN is exploited to configure the Remote Radio Heads (RRHs) to proper Base Band Unit (BBU) sectors in a time-varying traffic environment. RRH-sector configuration redistributes the network capacity over a given geographical area. A Self-Optimised Cloud Radio Access Network (SOCRAN) is considered to enhance the network QoS by traffic load balancing with minimum possible handovers in the network. QoS is formulated as an optimisation problem by defining it as a weighted combination of new key performance indicators (KPIs) for the number of blocked users and handovers in the network subject to RRH sectorisation constraint. A Genetic Algorithm (GA) and Discrete Particle Swarm Optimisation (DPSO) are proposed as evolutionary algorithms to solve the optimisation problem. Computational results based on three benchmark problems demonstrate that GA and DPSO deliver optimum performance for small networks, whereas close-optimum is delivered for large networks. The results of both GA and DPSO are compared to Exhaustive Search (ES) and K-mean clustering algorithms. The percentage of blocked users in a medium sized network scenario is reduced from 10.523% to 0.421% and 0.409% by GA and DPSO, respectively. Also in a vast network scenario, the blocked users are reduced from 5.394% to 0.611% and 0.56% by GA and DPSO, respectively. The DPSO outperforms GA regarding execution, convergence, complexity, and achieving higher levels of QoS with fewer iterations to minimise both handovers and blocked users. Furthermore, a trade-off between two critical parameters for the SOCRAN algorithm is presented, to achieve performance benefits based on the type of hardware utilised for C-RAN.en_US
dc.language.isoenen_US
dc.subjectBase Band Unit (BBU)en_US
dc.subjectCloud Radio Access Network (C-RAN)en_US
dc.subjectDiscrete Particle Swarm Optimisation (DPSO)en_US
dc.subjectGenetic Algorithm (GA),en_US
dc.subjectRemote Radio Head (RRH),en_US
dc.subjectSelf-Optimising Network (SON)en_US
dc.titleQoS-Aware dynamic RRH allocation in a Self-Optimised cloud radio access network with RRH proximity constrainten_US
dc.typeArticleen_US
dc.relation.isPartOfIEEE Transactions on Network and Service Management-
pubs.publication-statusAccepted-
Appears in Collections:Dept of Mechanical Aerospace and Civil Engineering Research Papers

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