Machine Learning and Analytical Power Consumption Models for 5G Base Stations
Nicola Piovesan, David López‐Pérez, Antonio De Domenico, Xinli Geng, Harvey Bao, Mérouane Debbah
Abstract
The energy consumption of the fifth generation (5G) of mobile networks is one of the major concerns of the telecom industry. However, there is not currently an accurate and tractable approach to evaluate 5G base stations' (BSs') power consumption. In this article, we propose a novel model for a realistic characterization of the power consumption of 5G multi-carrier BSs, which builds on a large data collection campaign. At first, we define a machine learning architecture that allows modeling multiple 5G BS products. Then we exploit the knowledge gathered by this framework to derive a realistic and analytically tractable power consumption model, which can help driving both theoretical analyses as well as feature standardization, development, and optimization frameworks. Notably, we demonstrate that this model has high precision, and it is able to capture the benefits of energy saving mechanisms. We believe this analytical model represents a fundamental tool for understanding 5G BSs' power consumption and accurately optimizing the network energy efficiency.