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
The emergence of AI-RAN infrastructure, which equips cell sites with GPU-accelerated hardware, creates an opportunity to colocate non-RAN workloads with primary RAN processing. We explore using this spare capacity for decentralized training of foundation models (FMs), one of the most compute-intensive AI workloads. We...
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.
We demonstrate Chronos, a cloud-based RAN emulator that allows running unmodified 4G/5G protocol stack software at large scale over commodity cloud VMs, using OpenAirInterface as the representative vRAN software. Chronos replaces compute-intensive PHY processing at the FAPI boundary, while employing time dilation and s...
Ujjwal Pawar, Andrew E. Ferguson, Yu-Feng Xia et al.· Proceedings of the 32nd Annu...· 0 citations
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