Machine-To-Machine Aware Downlink Scheduling Algorithms In Long Term Evolution

The capabilities to communicate and stay connected with people and their surroundings are the core of many tech gadgets exist today. Such phenomenon is nonetheless an evidence that wireless technologies have been evolving generation after generation, moving forward over years to meet demands with gr...

Full description

Saved in:

Bibliographic Details
Main Author:	Khoo, Siew Kay
Format:	Thesis
Published:	2018
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-mmu-ep.7744
record_format	uketd_dc
spelling	my-mmu-ep.77442020-09-21T07:22:58Z Machine-To-Machine Aware Downlink Scheduling Algorithms In Long Term Evolution 2018-06 Khoo, Siew Kay TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television The capabilities to communicate and stay connected with people and their surroundings are the core of many tech gadgets exist today. Such phenomenon is nonetheless an evidence that wireless technologies have been evolving generation after generation, moving forward over years to meet demands with greater capacity, speed and security. As promising connectivity option like Long Term Evolution (LTE) is made available, the industry of tech gadgets collectively known as Internet of Things (IoT) has grown so huge which it densely populated the wireless network with massive unique form of Machine-to-Machine (M2M) traffic loads. There also exist numerous wide-ranging unfamiliar services demanding to be seamlessly supported before proper considerations can be made. Concerned by how it upsets the former harmonious network environment, this research seeks for appropriate and more graceful measures to handle M2M in a hybrid network through future ready M2M-aware downlink scheduling motives. There are two different approaches be attempted in this work, conversion from existing methods to M2M-aware algorithms and formulation of novel M2M-aware scheduler from scratch. In the former approach, two latest conventional Bayesianbased schedulers have been selected to cater for M2M traffics while inherit their native abilities in handling uncertainty and multiple simultaneous user issues. Parameters within the algorithms are restructured in a manner that M2M traffic factors can be considered during traffic prioritisation to enable hybrid resource allocation procedure. 2018-06 Thesis http://shdl.mmu.edu.my/7744/ http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Information Science & Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Khoo, Siew Kay Machine-To-Machine Aware Downlink Scheduling Algorithms In Long Term Evolution
description	The capabilities to communicate and stay connected with people and their surroundings are the core of many tech gadgets exist today. Such phenomenon is nonetheless an evidence that wireless technologies have been evolving generation after generation, moving forward over years to meet demands with greater capacity, speed and security. As promising connectivity option like Long Term Evolution (LTE) is made available, the industry of tech gadgets collectively known as Internet of Things (IoT) has grown so huge which it densely populated the wireless network with massive unique form of Machine-to-Machine (M2M) traffic loads. There also exist numerous wide-ranging unfamiliar services demanding to be seamlessly supported before proper considerations can be made. Concerned by how it upsets the former harmonious network environment, this research seeks for appropriate and more graceful measures to handle M2M in a hybrid network through future ready M2M-aware downlink scheduling motives. There are two different approaches be attempted in this work, conversion from existing methods to M2M-aware algorithms and formulation of novel M2M-aware scheduler from scratch. In the former approach, two latest conventional Bayesianbased schedulers have been selected to cater for M2M traffics while inherit their native abilities in handling uncertainty and multiple simultaneous user issues. Parameters within the algorithms are restructured in a manner that M2M traffic factors can be considered during traffic prioritisation to enable hybrid resource allocation procedure.
format	Thesis
qualification_level	Master's degree
author	Khoo, Siew Kay
author_facet	Khoo, Siew Kay
author_sort	Khoo, Siew Kay
title	Machine-To-Machine Aware Downlink Scheduling Algorithms In Long Term Evolution
title_short	Machine-To-Machine Aware Downlink Scheduling Algorithms In Long Term Evolution
title_full	Machine-To-Machine Aware Downlink Scheduling Algorithms In Long Term Evolution
title_fullStr	Machine-To-Machine Aware Downlink Scheduling Algorithms In Long Term Evolution
title_full_unstemmed	Machine-To-Machine Aware Downlink Scheduling Algorithms In Long Term Evolution
title_sort	machine-to-machine aware downlink scheduling algorithms in long term evolution
granting_institution	Multimedia University
granting_department	Faculty of Information Science & Technology
publishDate	2018
_version_	1747829672093155328

Machine-To-Machine Aware Downlink Scheduling Algorithms In Long Term Evolution

Similar Items