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

• URL: http://arxiv.org/abs/2404.19671v1
• Date: Sun, 14 Apr 2024 17:48:05 GMT
• Title: ML-based handover prediction over a real O-RAN deployment using RAN Intelligent controller
• Authors: Merim Dzaferagic, Bruno Missi Xavier, Diarmuid Collins, Vince D'Onofrio, Magnos Martinello, Marco Ruffini,
• Abstract summary: We develop a use-case for open and reconfigurable networks to investigate the possibility to predict handover events. We propose a Long-Short Term Memory Machine Learning approach that takes standard Radio Access Network measurements to predict handover events. Our results show that ML-based matching between the required and available resources can reduce operational cost by more than 80%.
• Score: 4.464102544889847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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