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Title: QoS-Aware dynamic RRH allocation in a Self-Optimised cloud radio access network with RRH proximity constraint
Authors: Al-Raweshidy, H
Khan, M
Alhumaima, R
Keywords: Base Band Unit (BBU);Cloud Radio Access Network (C-RAN);Discrete Particle Swarm Optimisation (DPSO);Genetic Algorithm (GA),;Remote Radio Head (RRH),;Self-Optimising Network (SON)
Issue Date: 2017
Citation: IEEE Transactions on Network and Service Management, 2017
Abstract: An 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.
Appears in Collections:Dept of Mechanical and Aerospace Engineering Research Papers

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