The Role of AI Enablers in Overcoming Impairments in 6G Networks

The integration of Artificial Intelligence (AI) into the 6G architecture, referred to as AI-native 6G architecture, signifies a transformative era for communication technology. Nevertheless, practical implementation encounters challenges including architectural complexities, data quality concerns, and operational difficulties in managing machine learning models, allocating resources, and implementing intent-based management. In this paper, we present a comprehensive approach to address these challenges in emerging 6G networks through AI. Our approach involves two steps: first, we identify impairments hindering progress, analyzing the importance of addressing operational challenges in Machine Learning Operations (MLOps), 6G evolution, and democratizing AI, while addressing interoperability issues and complexities in the translation of business intents into network configurations. Upon the analysis, we highlight AI enablers—architectural enhancements, MLOps, Data Operations (DataOps), AI as a Service (AIaaS), and intent-based management—as essential solutions for practical AI implementation in 6G networks. We conclude by stating that architectural improvements prioritize privacy, security, and data accuracy, while MLOps and DataOps optimize the management of the AI life cycle. Privacy-aware data collection and training employ federated learning and split learning, and AIaaS streamlines AI access, and intent based management with integrated AI enhances decision-making through advanced algorithms.