Citation

Euttamarajah, Shornalatha (2023) Energy-efficient power management techniques for multi-tier heterogeneous networks. PhD thesis, Multimedia University.

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Abstract

Heterogeneous network (HetNet) has evolved into a blooming cellular communication technology ever since being introduced into the field. However, like most new technology, it is accompanied with some bottleneck issues which are interference, energy efficiency (EE) and complexity. The aim of this thesis is to integrate interference management and EE into a collective outcome and further intelligently reduce the complexity of the integration. After introducing the problems faced and reviewing some literature on the issues, the HetNet system model is developed and the said problems are investigated. First, the interference is tackled by coordinating the signal transmission of the base stations (BSs). Whereas the energy efficiency is achieved by harvested renewable energy cooperation among the access points. The proposed combinatorial optimisation scheme demonstrates better performance in terms of EE and grid power reduction. Then, the EE is further enhanced by switching off the less active BSs, where the criteria to switch off is set based on optimal and heuristic methods. At the same time, the computational complexity of both the methods are compared to prove the worth of introducing heuristic method. Additionally, the energy cooperation is efficiently managed by a centralised smart grid which connects the BSs. In order to reduce the complexity of the optimal solutions, in the next part, an artificial intelligence (AI) technique is introduced to the BS switching off scheme. The optimal BS switching scheme in the previous part is used to train the AI model in this section to achieve the best possible EE as the former. Meanwhile, the user distribution among the cells follows a random pattern rather than a uniform pattern which is more practical. Apart from demonstrating an improved EE performance, the computational complexity and time is also reduced heuristically and intelligently.

Item Type:	Thesis (PhD)
Additional Information:	Call No.: QA76.88 .S56 2023
Uncontrolled Keywords:	Heterogeneous computing
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Oct 2025 08:35
Last Modified:	03 Oct 2025 00:56
URII:	http://shdl.mmu.edu.my/id/eprint/14654

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