A Machine Learning Approach to Sleep Mode in Dense Cellular Networks
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The explosion of data hungry applications has led to a rapidly increasing demand for cellular network services. Mobile operators are responding by increasing the number of Base Stations (BSs) in their networks to enhance capacity. The densi cation of cellular networks however, comes with extensive capital and operational investment due to the increase in power consumption of the networks. In addition, this is raising environmental concerns over increase in greenhouse gas emissions. Moreover, current cellular networks are designed to handle peak traffic to other the best Quality of Service (QoS) at all times and yet network subscribers are not uniformly distributed making the traffic within the network vary spatiotemporarily. This results into subsequent energy losses throughout the day, which negatively affect the energy performance of the overall network. This research, therefore, aims to propose a machine learning approach to sleep modes in future cellular networks. Three machine learning (ML) algorithms, Random Forest (RF), Support Vector Regression (SVR) and Polynomial Regression (PR) were used to carry out traffic prediction and then compared. Results obtained from the different algorithms were analyzed using three performance metrics; accuracy, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). RF was selected since it gave the best results with 84% accuracy, and as such, the traffic pro les from this algorithm were adopted for sleep mode implementation on a homogeneous network. Simulation results show that the strategic sleep mode scheme performs best over a 24-hour period with a 50.12% energy saving gain over the conventional scheme and 15.55% energy saving gain over the random scheme. In addition, the strategic scheme achieves a 53:4 kW/km2 over a 24-hour period, which proves that ML traffic prediction-based sleep modes are instrumental in achieving energy efficient cellular networks.