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arXiv · 2601.02240

Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN

Matteo Bordin·Andrea Lacava·Michele Polese·Francesca Cuomo·Tommaso Melodia
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Abstract

The growing performance demands and higher deployment densities of next-generation wireless systems emphasize the importance of adopting strategies to manage the energy efficiency of mobile networks. In this demo, we showcase a framework that enables research on Deep Reinforcement Learning (DRL) techniques for improving the energy efficiency of intelligent and programmable Open Radio Access Network (RAN) systems. Using the open-source simulator ns-O-RAN and the reinforcement learning environment Gymnasium, the framework enables to train and evaluate DRL agents that dynamically control the activation and deactivation of cells in a 5G network. We show how to collect data for training and evaluate the impact of DRL on energy efficiency in a realistic 5G network scenario, including users' mobility and handovers, a full protocol stack, and 3rd Generation Partnership Project (3GPP)-compliant channel models. The tool will be open-sourced and a tutorial for energy efficiency testing in ns-O-RAN.

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Matteo Bordin, Andrea Lacava, Michele Polese, Francesca Cuomo, Tommaso Melodia. 2026-01-05. Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN. https://doi.org/10.1109/ccnc54725.2025.10975928

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