Machine Learning Models for Virtual Base Station Power Consumption Estimation

The growing demand for energy efficiency in mobile networks has driven the adoption of virtualized Base Stations running on general-purpose processors. This paper compares machine learning models for estimating power consumption using data from four processor architectures. Extreme Gradient Boosted Trees Regressions delivered the most accurate and robust predictions. Neural Networks exhibited unstable performance, particularly on specific platforms, whereas Linear Regressions demonstrated lower reliability in low-power scenarios. Results highlight the critical importance of aligning the processor architecture with the chosen model to ensure practical power estimation and energy optimization.