Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912805092851712 |
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| author | Bordin, Matteo Lacava, Andrea Polese, Michele Cuomo, Francesca Melodia, Tommaso |
| author_facet | Bordin, Matteo Lacava, Andrea Polese, Michele Cuomo, Francesca Melodia, Tommaso |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02240 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN Bordin, Matteo Lacava, Andrea Polese, Michele Cuomo, Francesca Melodia, Tommaso Networking and Internet Architecture 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. |
| title | Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2601.02240 |