Estimating Base Station Power Consumption Using Regression

Global warming is becoming a paramount concern in the world. One way to decrease the effect of global warming is by decreasing carbon emission and using renewable energy. In particular, there are many works on using renewable energy technologies in mobile communication systems. In order to enable such technologies in mobile communication systems, we should be able to estimate the required energy. Most of research was focusing on techniques to be used to exploit renewable energy sources assuming that the required energy to run the base stations is known. Only few works were focusing on the estimation of the energy based on transmitted energy, and fewer relating the former to traffic. In this paper, we present a regression-based power consumption estimation method based on voice and data traffic provided by base stations with 2G and 3G capabilities. Our results show that the power consumption of different base stations as a function of the provided traffic can have different patterns. Furthermore, the same base stations can have different energy consumption models at different period of time. Therefore, we advocate the use of machine learning algorithms inside each base station to learn its specific pattern.