Weng, Jialai (2016) On Advanced Channel Modelling for Network Planning. PhD thesis, University of Sheffield.
Abstract
With the increasing demand for high speed wireless network services, the next generation wireless networks are proposed to use advanced wireless communication technologies. These technologies include massive MIMO, mmWave and distributed MIMO. In order to deploy wireless networks equipped with these technologies, channel models capturing the channel features and characteristics of these wireless technologies are essential in the planning and optimisation of networks. However, conventional channel models lack the capability to support these next generation network technologies. In this PhD thesis, I investigated the channel models for the next generation wireless technologies, including massive MIMO, mmWave communications and distributed MIMO. I developed channel models for network planning and optimisation based on conventional ray launching algorithms for these wireless technologies. The models have been validated and applied to optimise network performance. The existing challenge in wireless channel modelling is the improvement of modelling accuracy without increasing modelling complexity. In order to achieve this goal, a new calibration method is developed to improve the accuracy of the predication model when measurements are available. Moreover, in order to use the channel models as an effective tool in wireless network planning and optimisation, a new wireless capacity definition from radio propagation perspective is also investigated. It provides insight to the physical limit of wireless channel capacity from a radio propagation perspective.
Metadata
Supervisors: | Zhang, Jie and Chu, Xiaoli |
---|---|
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Electronic and Electrical Engineering (Sheffield) |
Identification Number/EthosID: | uk.bl.ethos.762535 |
Depositing User: | Mr Jialai Weng |
Date Deposited: | 10 Dec 2018 08:53 |
Last Modified: | 23 Dec 2019 11:04 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:22361 |
Download
PHD Thesis
Filename: PHDthesis_FinalVersion.pdf
Description: PHD Thesis
Licence:
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 2.5 License
Export
Statistics
You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy.
You can contact us about this thesis. If you need to make a general enquiry, please see the Contact us page.