Given the current evolution trends in mobile cellular networks, which is approaching us towards the future 5G paradigm, novel techniques for network management are in the agenda. Machine Learning techniques are useful for extracting knowledge out of raw data; knowledge that can be applied to improving the experience in the operation of such systems. This paper proposes the use of Machine Learning applied to energy efficiency, which is set to be one major challenge in future network deployments. By studying the celllevel traces collected in a real network, we can study traffic patterns and derive predictive models for different cell load metrics with the aid of different machine learning techniques. Such models are applied into a simulation environment designed to test different algorithms which, according to cell load predictions, dynamically switch on and off base stations with the aim of providing energy savings in a mobile cellular network. Keywords-5G; machine learning; energy efficiencyI.
Abstract-Dense Small Cell networks are considered the most effective way to cope with the exponential increase in mobile traffic demand expected for the upcoming years and are one of the foundations of the future 5G. However, novel architectures are required to enable cost-efficient deployments of very dense outdoor Small Cell networks, complementing the coverage layer provided by macro-cells. In this regard, two important challenges need to be solved to make this vision a reality: i) increased traffic dynamics, which are translated into more frequent handovers, and ii) cost-efficient deployment of large number of Small Cells. In this paper we propose and evaluate SENSEFUL, an novel architecture addressing the two problems highlighted above: Software-Defined Networking (SDN) as the key technology to promote adaptability to a varying environment and provide efficient mobility solutions in the dense access layer, and novel wireless backhauling technologies where traditional wired connectivity does not meet cost/efficiency restrictions.
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