迈向 O-RAN 中由 AI/ML 驱动的 SMO 框架：场景、解决方案与挑战
Towards an AI/ML-driven SMO Framework in O-RAN: Scenarios, Solutions, and Challenges
AI总结：
本文面向 O-RAN 的 SMO 智能化需求，提出三类 ML 集成场景，并重点设计以 Non-RT RIC 为核心的集中式 ML 架构，同时分析实施挑战。
摘要：
开放无线接入网（O-RAN）架构的出现，借助数据驱动、基于意图、自主且智能的解决方案，为蜂窝网络管理和服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（NFs）、资源分配、服务供给等方面发挥着关键作用。然而，O-RAN 日益增长的复杂性和规模要求采用自主、智能的模型来优化 SMO 运营。为实现这一目标，必须将智能和自动化融入 SMO 的运营之中。在本文中，我们提出了三种将机器学习（ML）算法集成到 SMO 中的场景。随后，我们重点探讨其中一种场景：通过提出一种集中式 ML 架构，非实时 RAN 智能控制器（Non-RT RIC）在数据收集以及模型训练、部署和优化中发挥主要作用。最后，我们识别了在 SMO 内实施集中式 ML 解决方案所面临的潜在挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
