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.

翻译：人工智能（AI）已成为应对无线通信系统中复杂动态任务的有力工具。然而，该领域的研究主要聚焦于特定有限条件下的AI解决方案，这阻碍了模型学习并适应通用场景（例如无线通信系统中常见的各类情境）。本文强调实现模型泛化在提升性能及推动无线通信中可扩展AI集成方面的关键作用。我们围绕三个核心领域阐述了模型泛化的设计原则：面向鲁棒性的环境、面向系统目标适应性的意图，以及用于缩减AI驱动控制环路数量的控制任务。实施这些原则可减少部署模型的数量，并增强在不同无线通信环境中的适应性。为应对通信系统中模型泛化的挑战，我们提出了一种学习架构，该架构通过集中训练与数据管理功能，并结合分布式数据生成来实现。我们通过设计一种通用链路自适应算法来阐释这些概念，并展示了所提方法的优势。