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Intelligent on-demand radio resource provisioning for green ultra-small cell networks

Li, Zhehan (2016) Intelligent on-demand radio resource provisioning for green ultra-small cell networks. PhD thesis, University of York.

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Abstract

This thesis studies intelligent on-demand radio resource provisioning involving sleep mode operation in ultra Small Cell Networks (SCNs). Sleep modes are low power states of base stations. The purpose of the research is to investigate how appropriate traffic information can be adopted in sleep mode operation schemes for SCNs with different architectures. A novel protocol-friendly sleep mode operation algorithm based on Adaptive Traffic Perception is proposed for distributed SCN architectures. It is proved robust to different SCN layouts with the reduction in the average power consumption of base stations being more than 35% while maintaining the Quality of Service. The Traffic-aware Cell Management scheme adopting Direction of Arrival information is particularly designed to eliminate the necessity of computation for sleeping base stations. This scheme is shown to significantly reduce the side effects associated with the sleep mode operation, including system overheads and the increasing user transmission power. For SCNs using centralised architectures, such as Cloud Radio Access Networks, Hotspot-oriented Green Frameworks are proposed for different information availabilities, which achieve almost 80% reduction in power consumption of Remote Radio Heads at low traffic levels. A clustering technique is utilised for the optimisation of the placement of active Remote Radio Heads, lowering the average user transmission power. The amount of reduction depends on the completeness of the information and can exceed 70% compared with the state-of-the-art. A type II Matern Hard-core Point Process is used for modelling SCNs. The derivation and approximation of its distance distributions are also proposed. The distance distributions are used for the probabilistic theoretical analysis of some metrics of the sleep mode operation.

Item Type: Thesis (PhD)
Related URLs:
Academic Units: The University of York > Electronics (York)
Identification Number/EthosID: uk.bl.ethos.696095
Depositing User: Mr. Zhehan Li
Date Deposited: 15 Nov 2016 16:22
Last Modified: 24 Jul 2018 15:21
URI: http://etheses.whiterose.ac.uk/id/eprint/15506

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