为6G网络设计适配多元智能的MLOps框架，解决AI集成难题
Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions
- 提出RLOps、FedOps、GenOps三类操作管道应对不同学习范式
- 针对6G无线网络异构性，构建端到端的AI模型管理流程
- 适合关注6G智能演进与分布式AI部署的研究者和工程师
将人工智能（AI）与机器学习（ML）技术无缝融入无线系统是实现6G智能化的关键。然而，模型功能与生命周期管理仍面临挑战。机器学习运维（MLOps）为此提供系统性解决方案。现有集中式MLOps方案常忽视多样学习范式与网络异构性带来的问题。本文提出面向未来无线网络的新型MLOps方法，基于未来无线接入网（RAN）特点，构建三类操作管道：强化学习运维（RLOps）、联邦学习运维（FedOps）与生成式AI运维（GenOps）。这些管道构成将各类学习与推理能力融入网络的基础，明确各环节的具体挑战与解决方案，推动面向AI原生的6G网络大规模部署。
原文摘要 · Abstract (English)
Seamless integration of artificial intelligence (AI) and machine learning (ML) techniques with wireless systems is a crucial step for 6G AInization. However, such integration faces challenges in terms of model functionality and lifecycle management. ML operations (MLOps) offer a systematic approach to tackle these challenges. Existing approaches toward implementing MLOps in a centralized platform often overlook the challenges posed by diverse learning paradigms and network heterogeneity. This article provides a new approach to MLOps targeting the intricacies of future wireless networks. Considering unique aspects of the future radio access network (RAN), we formulate three operational pipelines, namely reinforcement learning operations (RLOps), federated learning operations (FedOps), and generative AI operations (GenOps). These pipelines form the foundation for seamlessly integrating various learning/inference capabilities into networks. We outline the specific challenges and proposed solutions for each operation, facilitating large-scale deployment of AI-Native 6G networks.
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