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Energy-efficient and QoS aware surplus resource allocation in OFDMA systems.

Sabagh, Mohammad R. (2017) Energy-efficient and QoS aware surplus resource allocation in OFDMA systems. Doctoral thesis, University of Surrey.

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Abstract

There has recently been a real demand to design and deploy mobile communication networks that consume significantly less energy compared to the existing s ystems. The main thrust of this research focuses on investigation of the impacts of radio resource allocation schemes in the current state-of-the-art Orthogonal Frequency Division Multiple Access (OFDMA) systems on energy efficiency (EE) o f modern Radio Access Networks ( RANs), a s well a s design of effective solutions to reduce RAN energy consumption in such networks. Due to data traffic fluctuation of communication networks, there are often many unused radio resource blocks in OFDMA systems. Efficient allocation of these surplus resource blocks can lead to considerable energy savings. One of the key objectives of this thesis is to exploit this opportunity by designing practical and effective radio resource allocation techniques that exploit fundamental trade-off between energy consumption and bandwidth by reducing energy consumption of the RAN while providing the required quality of service (QoS) for the network users. The basic concept here is to exploit fluctuations of data traffic in the network. Specifically, a novel e nergy e fficient re source al location te chnique, fo r low lo ad tr affic conditions is proposed. This technique is then applied to three bespoke scheduling schemes, namely Round Robin (RR), Best Channel Quality Indicator (BCQI), and Proportional Fair (PF) for performance assessment. Comprehensive evaluation of the proposed scheduling schemes demonstrates that adopting the proposed resource allocation technique significantly enhances the performance of RAN in terms of energy consumption in comparison with the conventional schemes such as the three aforementioned schedulers. Finding an optimal method for surplus resource allocation is firstly modelled as an optimisation problem which is subsequently solved using dynamic programming. In this context, a Knapsack Problem (KP) is adopted to find an optimal solution for a single-cell s cenario. The proposed heuristic method is simulated using Equal Power (EP) and Water Filling (WF) algorithms for surplus resource allocation. It is shown that the optimal solution is achieved using the WF algorithm leading to an EE saving of 60% compared to the greedy KP solution, whilst significantly lower computational complexity. The optimality of the proposed algorithm is evaluated in a multi-cell scenario to take into account realistic assumptions, which is more suitable for practical systems as interference of neighbouring cells are also considered. In this case, it is shown that the optimality of the proposed algorithm is valid and similar levels of energy saving can be achieved. Moreover, Cooperative Multi Point (CoMP) operation leading to interference mitigation and a coordinated resource allocation is considered to assess the optimality of the proposed algorithm. In this regard, two algorithms for assigning the surplus radio resources in OFDMA systems are proposed where a Multiple Choice bounded Knapsack problem (MCBKP) was defined to find the optimum values for bandwidth expansion of the scheduled users in each time slot. In addition to lower computational complexity and guaranteeing QoS, the percentage of energy saving by employing the optimum algorithm is 46%.

Item Type: Thesis (Doctoral)
Divisions : Theses
Authors :
NameEmailORCID
Sabagh, Mohammad R.UNSPECIFIEDUNSPECIFIED
Date : 31 August 2017
Funders : 5GIC
Contributors :
ContributionNameEmailORCID
http://www.loc.gov/loc.terms/relators/THSDianati, MehrdadM.Dianati@surrey.ac.ukUNSPECIFIED
http://www.loc.gov/loc.terms/relators/THSTafazolli, RahimR.Tafazolli@surrey.ac.ukUNSPECIFIED
Depositing User : Mohammad Reza Sabagh
Date Deposited : 07 Sep 2017 08:31
Last Modified : 07 Sep 2017 09:36
URI: http://epubs.surrey.ac.uk/id/eprint/841866

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