TWINERGY: Digital Twin Empowered Practical Energy Saving in Heterogeneous Mobile Networks

Abstract

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 offers large saving potential: it selectively switches off capacity cells in heterogeneous networks while a coverage layer preserves service. However, optimizing cell shutdown policies faces two key challenges: (i) live trial-and-error is prohibitively risky, while traditional simulators cannot capture the complex dynamics needed for reliable evaluation; (ii) operator-deployed threshold policies are interpretable but hard to optimize at scale. On the other hand, binary status optimization is more tractable but produces policies incompatible with operator workflows. We propose Twinergy, a digital twin-empowered framework for practical cell shutdown at scale. Twinergy builds a Network Digital Twin for safe virtual evaluation, and decouples policy design into two stages: first optimizing binary on/off decisions under QoS constraints, and then mapping them into deployable thresholds, with a dedicated module for each. For scalable decision optimization, we design Cable, a multi-agent reinforcement learning framework with shared policies, causal graph-based spatio-temporal encoding, and credit-guided cooperative learning. For threshold synthesis, we design a differentiable mapping module that introduces SoftControl as a surrogate for hard dual-threshold logic, enabling thresholds to be calibrated in continuous space via stable supervised optimization. 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.