Citation

Kamarul Hatta, Khairul Anwar (2016) Bayesian-Based Downlink Scheduling Algorithm for Long Term Evolution (LTE). Masters thesis, Multimedia University.

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Abstract

With the rising demand for faster internet connections for multimedia application features, LTE has been chosen as the medium of transmission to fulfil these demands. The need to handle multiple simultaneous users with real-time transmission containing either voice or audio is the challenge that LTE is now facing. Hence, this research is develop a new real-time oriented downlink scheduling algorithm with the capability of handling a multiple simultaneous user environment. A criterion-based (C-B) downlink scheduling algorithm is designed by incorporating a Bayesian information criterion (BIC) as the profit function of the algorithms. Criterion-based proportional fairness (C-BPF) is designed for the fairness focus, while another algorithm named criterion-based modified largest weighted delay first (C-BMLWDF), which adapts a profit function from an existing downlink scheduler, has also been developed. BIC is suitable for selection based on a set of criteria within a finite model. A new solution is found from the research, which leads to the use of a true Bayesian estimate (TBE) as the new solution’s profit function. TBE is capable of handling multivariate parameters in a large pool, which makes it better at solving multiple simultaneous user issues. Three TBE-based algorithms are created: true Bayesian estimate delay (TBE-D) focuses on delay prioritisation, true Bayesian estimate fairness (TBE-F) focuses on the fairness properties of the scheduling, and a balanced approach is made by true Bayesian flow delay (TBE-FD), which focuses on the delay in each flow as well as the number of flows of the current transmission.

Item Type:	Thesis (Masters)
Additional Information:	Call No.: QA76.9.A43 K43 2016
Uncontrolled Keywords:	Computer algorithms
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Sep 2017 10:44
Last Modified:	07 Sep 2017 10:44
URII:	http://shdl.mmu.edu.my/id/eprint/6893

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