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.
xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks
School of Electronic and Electrical Engineering, University of Leeds（利兹大学电子与电气工程学院）；College of Electronics Engineering, Ninevah University（尼尼微大学电子工程学院）
ORIX: Orchestration of RIS with xApps for Smart Wireless Factory Environments
HISAR Lab. of TUBITAK-BILGEM（土耳其科技工业研究委员会信息通信技术研究所海萨尔实验室）；Koç University（科奇大学）；Istanbul Medipol University（伊斯坦布尔医学大学）；Tampere Wireless Research Center, Tampere University（坦佩雷大学坦佩雷无线研究中心）；Turk Telekom R&D Department（土耳其电信研发部）；Istanbul Technical University（伊斯坦布尔理工大学）；EURECOM（欧洲通信研究所）
