Adapting MLOps for Diverse In-Network Intelligence in 6G Era: Challenges and Solutions
Abstract: Seamless integration of AI and 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.
- K. B. Letaief et al., “The Roadmap to 6G: AI Empowered Wireless Networks,” IEEE Commun. Mag., vol. 57, no. 8, pp. 84–90, 2019.
- “IMT-2030 Vision - International Telecommunication Union (ITU),” https://www.itu.int/en/ITU-R/study-groups/rsg5/rwp5d/imt-2030/Pages/default.aspx, accessed: July 02, 2024.
- F. Rezazadeh et al., “SliceOps: Explainable MLOps for Streamlined Automation-Native 6G Networks,” IEEE Wirel. Commun., 2024.
- 3GPP, “Study on 5G system support for AI/ML-based services,” 3rd Generation Partnership Project (3GPP), Technical Report (TR) 23.700-80, Release 18.
- ——, “Management and orchestration; Artificial Intelligence/ Machine Learning (AI/ML) management,” 3rd Generation Partnership Project (3GPP), Technical Specification (TS) 28.105, Release 17.
- ——. 3GPP Technologies - AI/ML Management for 5G Systems. Accessed: July 02, 2024. [Online]. Available: https://www.3gpp.org/technologies/ai-ml-management
- M. Polese et al., “Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges,” IEEE Commun. Surv. Tutor., vol. 25, no. 2, pp. 1376–1411, 2023.
- E. C. Strinati and S. Barbarossa, “6G networks: Beyond Shannon towards semantic and goal-oriented communications,” Computer Networks, vol. 190, p. 107930, 2021.
- D. Kreuzberger, N. Kühl, and S. Hirschl, “Machine Learning Operations (MLOps): Overview, Definition, and Architecture,” IEEE Access, 2023.
- P. Li et al., “RLops: Development Life-cycle of Reinforcement Learning Aided Open RAN,” IEEE Access, vol. 10, pp. 113 808–113 826, 2022.
- H. Li et al., “NetMind: Adaptive RAN Baseband Function Placement by GCN Encoding and Maze-solving DRL,” arXiv preprint arXiv:2401.06722, 2024.
- J. Moon, S. Yang, and K. Lee, “FedOps: A Platform of Federated Learning Operations with Heterogeneity Management,” IEEE Access, 2024.
- L. Bariah et al., “Large Generative AI Models for Telecom: The Next Big Thing?” IEEE Commun. Mag., 2024.
- P. Li and A. Aijaz, “Open RAN meets Semantic Communications: A Synergistic Match for Open, Intelligent, and Knowledge-Driven 6G,” in Proc. of IEEE CSCN, 2023, pp. 87–93.
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