Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in communication systems, where traditional rule-based algorithms often struggle. However, most AI applications to networking tasks are designed and trained for specific, limited conditions, hindering the algorithms from learning and adapting to generic situations, such as those met across radio access networks (RAN). This paper proposes design principles for sustainable and scalable AI integration in communication systems, focusing on creating AI algorithms that can generalize across network environments, intents, and control tasks. This approach enables a limited number of AI-driven RAN functions to tackle larger problems, improve system performance, and simplify lifecycle management. To achieve sustainability and automation, we introduce a scalable learning architecture that supports all deployed AI applications in the system. This architecture separates centralized learning functionalities from distributed actuation and inference functions, enabling efficient data collection and management, computational and storage resources optimization, and cost reduction. 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）已成为解决通信系统中传统基于规则算法难以应对的复杂动态任务的有力工具。然而，大多数面向网络任务的AI应用均针对特定有限条件进行设计和训练，这阻碍了算法学习并适应通用场景，例如无线接入网（RAN）中遇到的各类情境。本文提出了在通信系统中实现可持续且可扩展的AI集成的设计原则，重点关注创建能够跨网络环境、意图及控制任务进行泛化的AI算法。该方法使有限数量的AI驱动型RAN功能能够解决更大规模的问题、提升系统性能并简化生命周期管理。为达成可持续性与自动化，我们引入了一种支持系统中所有已部署AI应用的可扩展学习架构。该架构将集中式学习功能与分布式执行及推理功能相分离，从而实现高效的数据收集与管理、计算与存储资源优化以及成本降低。我们通过设计一种泛化链路自适应算法来阐述这些概念，并验证了所提方法的优势。