A New Explainable Power Demand Model for 4G LTE and 5G NR Base Stations

To design efficient Radio Access Networks (RANs) capable of handling the increasing power demand of modern networks, it is crucial to accurately assess the power consumption of a Base Station (BS). Since existing models are outdated, we propose a new data-driven model that accurately reflects the power demand of modern BSs. We derive the model using real-world measurements from operative three-sector BSs, differing for technologies and transmission frequencies. Our findings suggest that models tailored to specific technologies and transmission frequencies outperform generalized ones. Moreover, linear regression models consistently perform up to 96% better than those based on Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs) and benchmarks from the literature, highlighting a predominantly linear relationship between input features and power needs. Finally, the most accurate power estimations are produced by a linear model using traffic volume, load, maximum transmission power, and cable power losses, as regressors, with errors ranging from 4 W to 38 W.