Qazzaz, M.M.H., Kułacz, Ł, Kliks, A. et al. (3 more authors) (2024) Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks. In: 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN). 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), 05-08 May 2024, Stockholm, Sweden. IEEE , pp. 492-497. ISBN 979-8-3503-4320-5
Abstract
The disaggregated, distributed and virtualised implementation of radio access networks allows for dynamic resource allocation. These attributes can be realised by virtue of the Open Radio Access Networks (O-RAN) architecture. In this article, we tackle the issue of dynamic resource allocation using a data-driven approach by employing Machine Learning (ML). We present an xApp-based implementation for the proposed ML algorithm. The core aim of this work is to optimise resource allocation and fulfil Service Level Specifications (SLS). This is accomplished by dynamically adjusting the allocation of Physical Resource Blocks (PRBs) based on traffic demand and Quality of Service (QoS) requirements. The proposed ML model effectively selects the best allocation policy for each base station and enhances the performance of scheduler functionality in O-RAN- Distributed Unit (O-DU). We show that an xApp implementing the Random Forest Classifier can yield high (85%) performance accuracy for optimal policy selection. This can be attained using the O-RAN instance state input parameters over a short training duration.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | 5G, RAN, O-RAN, Resource Allocation, xApp, AI/ML |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Mar 2025 13:28 |
Last Modified: | 12 Mar 2025 13:28 |
Published Version: | https://ieeexplore.ieee.org/document/10625184 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/icmlcn59089.2024.10625184 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224315 |