Use of Machine Learning for energy efficiency in present and future mobile networks
Sesto Castilla, David; García Villegas, Eduard; Lyberopoulos, George; Theodoropoulou, Eleni
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 cell-level 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.
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