On Intelligent Base Station Activation for Next Generation Wireless Networks.

Abstract The evolution of mobile communications, during the last decades, has led to a rapid increase in the number of users that mobile operators have to serve. To cope with this increase, mobile operators increment the number of base stations they are using resulting in an escalation of the corresponding energy footprint. This is why; the reduction of the total energy that is consumed from base stations has been the epicentre of many researchers. To achieve that, a common approach is to minimize the number of base stations that are used by activating only the necessary base stations without affecting the corresponding quality of service. In this paper, we present a method for predicting crowded areas based on machine learning techniques. The dataset used contains information about the number of users that have been connected to twenty base stations during the time period of 8 days. Prediction results can be used in order to make appropriate suggestions to mobile operators about bases stations that can be activated or deactivated. We propose a Probabilistic Neural Network and confirm its superior performance against two other types of neural networks.