Learning to Optimize Voltage Level for Energy-Efficient Radios

Journal article, 2026

Reducing energy waste in cellular radio systems is critical for lowering the carbon footprint of wireless communication networks. In current deployments, radio power amplifiers (PAs) typically operate at a fixed high supply voltage regardless of physical resource block utilization (PRB-U), leading to unnecessary energy consumption. This work investigates a machine learning-based approach on real field data for dynamically switching the PA supply voltage between two hardware-supported levels, thereby adapting the available radio-frequency (RF) output power capacity to predicted traffic demand. While many prior approaches focus on network-level control, the proposed method performs voltage optimization at the level of individual radio units. Eight models are evaluated under two task formulations: 1) classification of voltage levels and 2) regression of PRB-U followed by mapping to voltage states. The results show that machine learning can achieve substantial energy savings, although effectiveness varies across radios. Underestimations, defined as instances where the predicted voltage is lower than required, are minimized more effectively by classification models than by regression approaches, resulting in a more reliable trade-off between energy efficiency and connectivity. A customized Feedforward Neural Network classifier and a Random Forest model perform best, outperforming both statistical baselines and more complex temporal architectures, achieving F1 scores of 0.35 and 0.29 respectively while reducing energy consumption by over 12%. In contrast, a baseline ARIMA model shows poor predictive performance with an F1 score of 0.09 despite similar energy savings. Explainability analysis using SHAP indicates that current PRB-U is the most influential feature, while the superior performance of nonlinear models suggests that linear utilization-based approaches are insufficient for reliable switching decisions. These results demonstrate the potential of learning-based PA voltage control as a new mechanism for improving the energy efficiency of cellular radio systems while maintaining service reliability.