Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks
This addresses the challenge of scalable AI integration in radio access networks, but it appears incremental as it builds on existing AI solutions.
The paper tackles the problem of AI models lacking generalization in radio communication systems by proposing design principles and a learning architecture, which can decrease deployed models and increase adaptability.
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.