The emergence of the open radio access network (O-RAN) architecture offers a paradigm shift in cellular network management and service orchestration, leveraging data-driven, intent-based, autonomous, and intelligent solutions. Within O-RAN, the service management and orchestration (SMO) framework plays a pivotal role in managing network functions (NFs), resource allocation, service provisioning, and others. However, the increasing complexity and scale of O-RANs demand autonomous and intelligent models for optimizing SMO operations. To achieve this goal, it is essential to integrate intelligence and automation into the operations of SMO. In this manuscript, we propose three scenarios for integrating machine learning (ML) algorithms into SMO. We then focus on exploring one of the scenarios in which the non-real-time RAN intelligence controller (Non-RT RIC) plays a major role in data collection, as well as model training, deployment, and refinement, by proposing a centralized ML architecture. Finally, we identify potential challenges associated with implementing a centralized ML solution within SMO.

翻译：开放无线接入网（O-RAN）架构的出现，通过利用数据驱动、基于意图、自主且智能的解决方案，为蜂窝网络管理与业务编排带来了范式转变。在O-RAN中，服务管理与编排（SMO）框架在管理网络功能（NF）、资源分配、业务供应等方面起着关键作用。然而，O-RAN日益增长的复杂性与规模要求采用自主智能模型来优化SMO的运营。为实现这一目标，将智能与自动化集成到SMO的运营中至关重要。在本文中，我们提出了将机器学习（ML）算法集成到SMO中的三种场景。随后，我们重点探讨了其中一种场景，即非实时RAN智能控制器（Non-RT RIC）在数据收集以及模型训练、部署与优化中发挥主要作用，并为此提出了一种集中式ML架构。最后，我们指出了在SMO内部实施集中式ML解决方案可能面临的潜在挑战。