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...
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| Format: | Thesis |
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2018
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| Summary: | 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. |
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