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 energy-constrained 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 ad-vantages 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).
|Title of host publication||IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)|
|Place of Publication||Piscataway|
|Number of pages||2|
|Publication status||Published - 2021|
|Event||IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) - Virtual/Vancouver, Canada|
Duration: 10 May 2021 → 13 May 2021
|Workshop||IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)|
|Period||10/05/21 → 13/05/21|
Bibliographical noteGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
- Power demand
- Machine learning algorithms
- Machine learning
- Bayes methods