Cell shutdown is a critical mechanism for reducing mobile network energy consumption, but optimizing it remains difficult in practice. Live trial-and-error can overload cells and degrade service; currently deployed threshold-based policies are interpretable but hard to optimize at scale. This paper presents Twinergy, w...
Yi-Bo Ma, Tian-Xin Wang, Hao-Ye Chai et al.· Proceedings of the 32nd Annu...· 0 citations
ZoomDiff is proposed, a diffusion-based model for multi-scale mobile traffic generation that maps urban environmental context into mobile traffic with multiple spatial and temporal resolutions through a set of customized Denoising Refinement Diffusion Models (DRDM).
Xiao-Qian Qi, Hao-Ye Chai, Si-Chang Liu et al.· IEEE Transactions on Mobile...· 0 citations
A physics-guided two-stage training scheme with a noise prior guidance mechanism, enabling effective fusion of multi-scale physical knowledge with the diffusion models, resulting in a diffusion-model-based RSRP prediction model, Channel-Diff.
Xiao-Qian Qi, Hao-Ye Chai, Yue Wang et al.· IEEE Transactions on Mobile...· 0 citations
Twinergy, a digital twin-empowered framework for practical cell shutdown at scale, and Cable, a multi-agent reinforcement learning framework with shared policies, causal graph-based spatio-temporal encoding, and credit-guided cooperative learning are proposed.
