Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs

Radio Access Network Virtualization (vRAN) will

spearhead the quest towards supple radio stacks that adapt to

heterogeneous infrastructure: from energy-constrained platforms

deploying cells-on-wheels (e.g., drones) or battery-powered cells

to green edge clouds. We demonstrate a novel machine learning

approach to solve resource orchestration problems in energyconstrained

vRANs. Specifically, we demonstrate two algorithms:

(i) BP-vRAN, which uses Bayesian online learning to balance

performance and energy consumption, and (ii) SBP-vRAN,

which augments our Bayesian optimization approach with safe

controls that maximize performance while respecting hard power

constraints. We show that our approaches are data-efficient—

converge an order of magnitude faster than other machine

learning methods—and have provably performance, which is

paramount for carrier-grade vRANs. We demonstrate the advantages

of our approach in a testbed comprised of fully-fledged

LTE stacks and a power meter, and implementing our approach

into O-RAN’s non-real-time RAN Intelligent Controller (RIC).