ML-based Predictive Models for Power Consumption in Virtualised O-RANs
2026-07-27 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors studied how to predict power use in virtualized open radio access networks (O-RANs), which are more dynamic and complex than traditional networks. They compared three deep learning approaches, including a hybrid model that combined deep neural networks with XGBoost. Their tests showed the hybrid model predicted energy consumption most accurately, with very low error. This approach could help future networks manage energy use more efficiently.
Open Radio Access Network (O-RAN)Power modelingEnergy efficiencyDeep Neural Networks (DNN)XGBoostVirtualized networksMachine learningFeature extractionModulation coding schemesTransmission gain
Authors
Rishu Raj, Genevieve Akude, Urooj Tariq, Daniel Kilper
Abstract
As communication networks adopt virtualized and disaggregated architectures, achieving energy efficiency has become increasingly important for both economic and environmental reasons. Traditional methods for power modeling are inadequate in these dynamic software-defined environments due to their inability to model complex and nonlinear factors affecting energy use. We investigate the use of feature extraction and regressor-based machine learning methods for predicting power consumption in virtualized open radio access networks (O-RANs), utilizing datasets from a hardware-instrumented testbed. We test three variants of deep neural networks (DNNs), namely, a standard DNN, a regularized DNN, and a hybrid model combining DNN-based feature extraction with an XGBoost regressor. We evaluate the performance of these models for various system parameters such as transmission gain, modulation/coding schemes, and airtime. We show that the hybrid model consistently outperformed others, achieving a mean relative error below 0.5%. Results suggest hybrid models like DNN-XGBoost offer superior accuracy and could be integrated into O-RAN management tools to enable more energy-efficient network orchestration in future networks.