Research Area:  Mobile Computing

Abstract:

   Fifth Generation (5G) cellular networks will experience a thousand-fold increase in data traffic with over 100 billion connected devices by 2020. In order to support this skyrocketing traffic demand, smaller base stations (BSs) are deployed to increase capacity. However, more BSs increases energy consumption which contributes to operational expenditure (OPEX) and CO2 emissions. Also, an introduction of a plethora of 5G applications running in the mobile devices causes a significant amount of energy consumption in the mobile devices.
   This thesis presents a novel framework for energy efficiency in 5G cloud radio access networks (C-RAN) by leveraging cloud computing technology. Energy efficiency is achieved in three ways; (i) at the radio side of H-C-RAN (Heterogeneous C-RAN), a dynamic BS switching off algorithm is proposed to minimize energy consumption while maintaining Quality of Service (QoS), (ii) in the BS cloud, base band workload consolidation schemes are proposed based on simulated annealing and genetic algorithms to minimize energy consumption in the cloud, where also advanced fuzzy based admission control with preemption is implemented to improve QoS and resource utilization (iii) at the mobile device side, Mobile Edge Computing (MEC) is used where computer intensive tasks from the mobile device are executed in the MEC server in the cloud.

Name of the Researcher:  Sigwele, Tshiamo

Name of the Supervisor(s):  Pillai, Prashant

Year of Completion:  2017

University:  University of Bradford

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