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

TL;DR

This paper demonstrates that ML-based handover prediction using RAN measurements in an O-RAN setup can improve network efficiency and reduce operational costs significantly.

Contribution

It introduces a LSTM-based approach for handover prediction in real O-RAN deployments, linking ML performance to operational cost savings.

Findings

ML model can be optimized for recall or precision.

Handover prediction reduces operational costs by over 80%.

Real network data was used for training and validation.

Abstract

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.