Energy consumption has become a pressing concern as mobile networks expand rapidly, with the radio access network accounting for the dominant share. Among energy-saving techniques, cell shutdown stands out as a critical mechanism for reducing mobile network energy consumption, but optimizing it remains difficult in deployments. Live trial-and-error can overload cells and degrade service; currently deployed threshold-based policies are interpretable but hard to optimize at scale. We present Twinergy, which searches for safe binary shutdown decisions in a high-fidelity network digital twin and maps them into thresholds for currently deployed threshold-based policies. Twinergy combines a data- and knowledge-driven digital twin, scalable multi-agent reinforcement learning (RL), and differentiable threshold mapping.

Experiments on a digital twin built from 14,349 real-world base stations and deployments across three commercial trial networks with over 3,400 cells show that Twinergy achieves up to 26.0% and 12.4% energy savings in 4G and 5G digital-twin evaluation, respectively, and delivers up to 13.07% and 12.20% energy-efficiency improvements in rural and dense 5G live networks without degrading QoS.

About Tianxin Wang

Tianxin Wang is a Postdoctoral Research Associate at the School of Informatics, University of Edinburgh, working with Prof. Mahesh Marina. Her research interests include AI for radio access networking, mobile network optimization, and Open RAN systems. She received her Ph.D. from Shanghai Jiao Tong University in 2025, with two years of joint training at Imperial College London (2023 – 2025).

This event will be conducted in English