Joint optimization of cell zooming, scheduling and user association

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2018
Çalık, Mert
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Capacity can be increased by employing small cells in future mobile networks. When small cells are considered, a large number of base stations have to be deployed. This approach enlarges network infrastructures and increases the amount of energy consumption. Traffic demand in a mobile network is not fixed in time or space, and it cannot be accurately predicted in advance. Network functions such as base station scheduling, cell zooming or user-to-base-station association can be dynamically controlled to conserve energy. Base station scheduling is defined as deciding whether or not to keep a base station active depending on traffic demand. The coverage area of a base station can be adapted to demand that is referred to as cell zooming. User-to-base-station association can be dynamically configured to adjust the load of base stations for reducing energy consumption. Most of the related work in the literature consider these problems separately. In this thesis, we present a joint base station scheduling, zooming and user association technique. The major contribution of this thesis is reducing power consumption by turning off redundant base stations and adapting transmit power of active base stations to network conditions for maintaining quality of service and satisfying users. We formulated the joint optimization problem with the objective of reducing energy consumption and enhancing user satisfaction. We validated the proposed technique by using MATLAB optimization toolbox. In this work, we reduced energy consumption by 47% in comparison to the state-of-the-art solution.
Linear programming., Wireless communication system., Genetic algorithms.
http://etd.lib.metu.edu.tr/upload/12621887/index.pdf
https://hdl.handle.net/11511/27159
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
M. Çalık, “Joint optimization of cell zooming, scheduling and user association,” M.S. - Master of Science, Middle East Technical University, 2018.
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