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Forecasting Mobile Traffic to Achieve Greener 5G Networks: When Machine Learning is Key

To minimise the energy consumption of the fifth generation (5G) of mobile technology, it is necessary to adapt the transmission capabilities of 5G networks to the end-users’ quality of service (QoS) requirements. In this line, researchers are investigating novel machine learning (ML) algorithms, which fed by network measurement data and empowered by the computing capabilities of dedicated hardware, can help modelling and predicting user behaviour. In this paper, we focus on the task of forecasting mobile traffic, which is a key step to dynamically adapt network parameters to space-time traffic variations by optimizing energy efficiency features, such as carrier shutdown. Specifically, we present a state-of-the-art ML framework based on Graph convolutional Networks, and compare its performance with simpler schemes, characterized by a lower complexity. Our analysis highlights advantages and drawbacks of these solutions, paying special attention to the importance of the error metric selection, feature pre-processing and historical data availability.