With the astronomical increase in cellular traffic, there is need to cut down on the power consumption so as to reduce CO2 emissions and in the process lowering network operational expenditure (OPEX). In this thesis, one method that can be used to lower a Base Station energy consumption is proposed. Traditional cellular networks are designed to offer maximum coverage and connectivity for peak traffic. This is not energy efficient since a lot of energy will go to waste during the time cellular traffic is very low. The scheme that was developed, identifies Base Stations that have very low traffic loads and User Equipment that can all be transferred to neighbouring Base Stations and put the Base Stations to sleep for as long as necessary to save energy and to maintain Quality of Services (QoS) at an acceptable level. The Next Generation Networks (specifically 5G) will be heterogeneous networks as heterogeneous are a promising solution in increasing network performance especially in providing indoor and cell-edges coverage. The solution that was developed in this thesis was specifically designed to work with heterogeneous networks and its performance was also tested on heterogeneous networks. OMNeT++ V4.6 together with INET 2.3.0 and SimuLTE 9.1 were used for the validation of the proposed scheme. After extensive simulations were carried out, it was concluded that some Base Stations in a cellular network, can be put to sleep during the time that cellular traffic is low without compromising the Quality of Service. End-to-end delay, sum throughputs, queue length and Channel Quality Indicator were some of the performance metrics that were used to check whether the developed scheme did not reduce the QoS of a network. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/22965 |
| Date | January 2016 |
| Creators | Mwashita, Weston |
| Contributors | Ohanga, M. O. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (xii, 100 leaves) : color illustrations |
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