University of Oulu

F. W. Murti, S. Ali, G. Iosifidis and M. Latva-Aho, "Learning-Based Orchestration for Dynamic Functional Split and Resource Allocation in vRANs," 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 2022, pp. 243-248, doi: 10.1109/EuCNC/6GSummit54941.2022.9815815.

Learning-based orchestration for dynamic functional split and resource allocation in vRANs

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Author: Murti, Fahri Wisnu1; Ali, Samad1; Iosifidis, George2;
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
2Delft University of Technology, Netherlands
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022091959519
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-09-19
Description:

Abstract

One of the key benefits of virtualized radio access networks (vRANs) is network management flexibility. However, this versatility raises previously-unseen network management challenges. In this paper, a learning-based zero-touch vRAN orchestration framework (LOFV) is proposed to jointly select the functional splits and allocate the virtualized resources to minimize the long-term management cost. First, testbed measurements of the behaviour between the users’ demand and the virtualized resource utilization are collected using a centralized RAN system. The collected data reveals that there are non-linear and non-monotonic relationships between demand and resource utilization. Then, a comprehensive cost model is proposed that takes resource overprovisioning, declined demand, instantiation and reconfiguration into account. Moreover, the proposed cost model also captures different routing and computing costs for each split. Motivated by our measurement insights and cost model, LOFV is developed using a model-free reinforcement learning paradigm. The proposed solution is constructed from a combination of deep Q-learning and a regression-based neural network that maps the network state and users’ demand into split and resource control decisions. Our numerical evaluations show that LOFV can offer cost savings by up to 69% of the optimal static policy and 45% of the optimal fully dynamic policy.

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Series: European Conference on Networks and Communications
ISSN: 2475-6490
ISSN-E: 2575-4912
ISSN-L: 2475-6490
ISBN: 978-1-6654-9871-5
ISBN Print: 978-1-6654-9872-2
Pages: 243 - 248
DOI: 10.1109/EuCNC/6GSummit54941.2022.9815815
OADOI: https://oadoi.org/10.1109/EuCNC/6GSummit54941.2022.9815815
Host publication: 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
Conference: 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
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