Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks

TL;DR

This paper presents a machine learning-based xApp for dynamic resource allocation in O-RAN networks, optimizing PRB distribution based on traffic and QoS to improve scheduler performance.

Contribution

It introduces a novel ML-driven xApp implementation using Random Forest for real-time resource allocation in O-RAN, achieving high accuracy with minimal training.

Findings

Random Forest xApp achieves 85% accuracy in policy selection

Effective dynamic PRB allocation improves QoS adherence

Short training duration suffices for high performance

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.