  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.
