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.

利用 O-RAN 的智能和灵活的网络控制，本研究介绍了一种基于开放和可重构网络的用例，以预测切换事件，并理解此类预测对所有依赖通信网络进行业务的利益相关者的价值。我们提出了一种基于长短期记忆机器学习方法，使用标准无线接入网络测量数据来预测切换事件。通过在真实网络数据上进行训练，我们的结果表明所提出的方法可以根据应用层目标进行优化，以提高精度或召回率。我们还将机器学习算法的性能与网络运营成本联系起来，结果表明相比长期资源购买，基于机器学习的资源匹配可以将运营成本降低 80% 以上。