Abstract
Abstract: Mobile networks' energy consumption is rising in tandem with the volume of traffic and the number of people utilising mobile technology. To maintain the long-term survival of the next generation of mobile networks, there must be an emphasis on energy efficiency. By minimising the network's power consumption and proposing an energy-efficient network architecture, this thesis addresses the issue of increasing 5G and beyond network efficiency. The first component of this thesis focuses on base stations (BSs), the most energy-intensive part of mobile networks. Mobile network providers offer us with a data set that contains information on the amount of traffic on their system. The poor temporal granularity of mobile network traffic data makes it difficult to train ML systems for sleep mode management choices. Bursty arrivals are taken into consideration while generating mobile network traffic statistics.
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