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

arXiv: https://arxiv.org/abs/2401.07643 · HTML: https://mlsanity.com/paper/2401.07643/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2024.

## Built on by (most cited first, 5 of 5)

- [What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems](https://mlsanity.com/paper/2410.00150/) (2024)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [Optimizing Search and Rescue UAV Connectivity in Challenging Terrain through Multi Q-Learning](https://mlsanity.com/paper/2405.10042/) (2024)
- [Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning](https://mlsanity.com/paper/2401.08861/) (2024)
- [AI Lifecycle-Aware Feasibility Framework for Split-RIC Orchestration in NTN O-RAN](https://mlsanity.com/paper/2603.23252/) (2026)

## Abstract (arXiv)

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.