Optimization of Base Station ON-Off Switching with a Machine Learning Approach

The next mobile generation is highly expected since it is supposed to increase the bit rate and reduce latency to allow multiple new services been offered. However, there is a big concern about energy efficiency, since, more base stations must be added, providing amplified coverage and higher spectral efficiency. There have been several algorithms designed to reduce the energy consumption of wireless networks by switching ON/OFF the small cells inside a Macro-cell without risking the bit rate received by the users. However, the resource allocation issues involved, e.g. base station selection and user association are NP-hard in general. In this paper we propose a novel solution, applying machine learning to train two neural networks to predict which base stations are not critical and can start their sleeping mode and also predict the associations between the users and base stations giving a complete solution for optimizing the energy efficiency in wireless networks. The outcome will provide similar results to the mathematical optimization but saving 99% of the time spent.