Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks

TL;DR

The paper tackles the challenge of deploying AI in radio access networks by reframing model generalization as a design imperative across three domains: environment robustness, intent-driven adaptability, and control-task consolidation. It proposes a scalable, centralized-learning with distributed-data-generation architecture (Learning Engine and Data Engine) to enable broad generalization and efficient data use, validated through a generalized link adaptation algorithm. Key contributions include a state-reconstruction approach separating static/dynamic RAN information, an intent-based MORL framework to balance KPIs, and distributed RL architectures (Ape-X–style) to scale training while maintaining diversity. The work demonstrates significant performance gains and practical pathways for reducing AI proliferation across RANs, with concrete mappings to real-world architectures and deployment considerations.

Abstract

Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in radio communication systems. Research in this area, however, focused on AI solutions for specific, limited conditions, hindering models from learning and adapting to generic situations, such as those met across radio communication systems. This paper emphasizes the pivotal role of achieving model generalization in enhancing performance and enabling scalable AI integration within radio communications. We outline design principles for model generalization in three key domains: environment for robustness, intents for adaptability to system objectives, and control tasks for reducing AI-driven control loops. Implementing these principles can decrease the number of models deployed and increase adaptability in diverse radio communication environments. To address the challenges of model generalization in communication systems, we propose a learning architecture that leverages centralization of training and data management functionalities, combined with distributed data generation. We illustrate these concepts by designing a generalized link adaptation algorithm, demonstrating the benefits of our proposed approach.

Figures (5)

• Figure 1: A framework for model generalization in radio systems over network environment, intents, and control.
• Figure 2: An architecture for scalable integration of AI in RAN systems featuring centralized learning and data services to AI-driven network functionalities distributed in the underlying RAN.
• Figure 3: Performance of model generalized over network environment in benchmark scenarios featuring MIMO, mMIMO, and heterogeneous deployments with full buffer (FB), mobile broadband (MBB), or mixed traffic.
• Figure 4: Evaluation of model generalization over LA control actions in an SU-MIMO setup.
• Figure 5: Evaluation of model generalization over MAC intents to flexibly adapt LA parameters to meet specific intents.