Abstract
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).
Original language | English |
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Title of host publication | IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 1-2 |
Number of pages | 2 |
ISBN (Electronic) | 978-1-6654-0443-3 |
ISBN (Print) | 978-1-6654-4714-0 |
DOIs | |
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
Workshop | IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) |
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Country/Territory | Canada |
City | Virtual/Vancouver |
Period | 10/05/21 → 13/05/21 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise 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.
Keywords
- Meters
- Power demand
- Machine learning algorithms
- Conferences
- Machine learning
- Bayes methods
- Virtualization