# ML-based handover prediction over a real O-RAN deployment using RAN Intelligent controller

arXiv: https://arxiv.org/abs/2404.19671 · HTML: https://mlsanity.com/paper/2404.19671/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing](https://mlsanity.com/paper/2208.01736/) (2022)
- [Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)](https://mlsanity.com/paper/2201.07385/) (2022)
- [Cloud-RAN functional split for an efficient fronthaul network](https://mlsanity.com/paper/2101.04216/) (2021)
- [ML Approach for Power Consumption Prediction in Virtualized Base Stations](https://mlsanity.com/paper/2301.05764/) (2023)
- [Minimizing the Signaling Overhead and Latency based on Users' Mobility Patterns](https://mlsanity.com/paper/1902.01942/) (2019)

## Abstract (arXiv)

O-RAN introduces intelligent and flexible network control in all parts of the network. The use of controllers with open interfaces allow us to gather real time network measurements and make intelligent/informed decision. The work in this paper focuses on developing a use-case for open and reconfigurable networks to investigate the possibility to predict handover events and understand the value of such predictions for all stakeholders that rely on the communication network to conduct their business. We propose a Long-Short Term Memory Machine Learning approach that takes standard Radio Access Network measurements to predict handover events. The models were trained on real network data collected from a commercial O-RAN setup deployed in our OpenIreland testbed. Our results show that the proposed approach can be optimized for either recall or precision, depending on the defined application level objective. We also link the performance of the Machine Learning (ML) algorithm to the network operation cost. Our results show that ML-based matching between the required and available resources can reduce operational cost by more than 80%, compared to long term resource purchases.