The need for intelligent and efficient resource provisioning for the productive management of resources in real-world scenarios is growing with the evolution of telecommunications towards the 6G era. Technologies such as Open Radio Access Network (O-RAN) can help to build interoperable solutions for the management of complex systems. Probabilistic forecasting, in contrast to deterministic single-point estimators, can offer a different approach to resource allocation by quantifying the uncertainty of the generated predictions. This paper examines the cloud-native aspects of O-RAN together with the radio App (rApp) deployment options. The integration of probabilistic forecasting techniques as a rApp in O-RAN is also emphasized, along with case studies of real-world applications. Through a comparative analysis of forecasting models using the error metric, we show the advantages of Deep Autoregressive Recurrent network (DeepAR) over other deterministic probabilistic estimators. Furthermore, the simplicity of Simple-Feed-Forward (SFF) leads to a fast runtime but does not capture the temporal dependencies of the input data. Finally, we present some aspects related to the practical applicability of cloud-native O-RAN with probabilistic forecasting.

翻译：随着电信技术向6G时代演进，在实际场景中对资源进行智能化高效供给以实现生产性资源管理的需求日益增长。开放无线接入网络（O-RAN）等技术有助于构建管理复杂系统的互操作解决方案。与确定性单点估计器相比，概率预测可通过量化生成预测的不确定性，为资源分配提供一种不同的方法。本文探讨了O-RAN的云原生特性及其无线应用（rApp）部署方案，重点阐述了概率预测技术作为rApp在O-RAN中的集成方式，并结合实际应用案例进行分析。通过使用误差指标对预测模型进行对比分析，我们证明了深度自回归循环网络（DeepAR）相较于其他确定性概率估计器的优势。此外，简单前馈网络（SFF）虽具有运行速度快的优点，但无法捕捉输入数据的时间依赖性。最后，我们讨论了云原生O-RAN结合概率预测在实际应用中的若干关键问题。