Driven by the demands of efficient network operation and high service availability, the convergence of artificial intelligence (AI) with radio access networks (RANs) has drawn considerable attention. However, current academic research mainly focuses on applying AI into optimizing RANs with a few discussions on architecture design. This article surveys the recent progress achieved by industry in integrating AI into RANs, and proposes an AI-driven fog RAN (F-RAN) paradigm. Specifically, being wrappers of Al-re-lated functionalities, AI capsules are presented as new network functions in the F-RAN domain. With AI capsules, computation and cache resources at various fog nodes can be utilized to facilitate real-time AI-based F-RAN optimization and alleviate the transmission burden incurred by network data collection. At the edge cloud, a centralized AI brain for F-RANs is deployed, which incorporates a wireless-oriented auto-AI platform and a digital colon of the network environment for offline AI model training and evaluation. By the interplay among AI capsules and the AI brain, universal and endogenous intelligence can be fully realized within F-RANs, which in turn enhances system performance. Furthermore, we demonstrate the effectiveness of a scalable deep-reinforcement-learning-based method in minimizing energy consumption for a computation offloading use case. At last, open issues are identified in terms of interface standardization, federated learning, and transfer learning.

中文翻译：

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人工智能与无线电接入网络的协调：进展、案例研究和悬而未决的问题


在网络高效运营和高服务可用性需求的推动下，人工智能（AI）与无线接入网络（RAN）的融合引起了广泛关注。然而，目前的学术研究主要集中在将人工智能应用于优化RAN，对架构设计的讨论很少。本文调查了业界在将 AI 集成到 RAN 方面取得的最新进展，并提出了 AI 驱动的雾 RAN (F-RAN) 范式。具体来说，作为 AI 相关功能的包装，AI 胶囊在 F-RAN 域中作为新的网络功能呈现。通过AI胶囊，可以利用各个雾节点的计算和缓存资源，促进基于AI的F-RAN实时优化，减轻网络数据收集带来的传输负担。在边缘云，部署了F-RAN的集中式AI大脑，其中包含面向无线的自动AI平台和用于离线AI模型训练和评估的网络环境的数字冒号。通过AI胶囊和AI大脑之间的相互作用，F-RAN可以充分实现通用的内生智能，从而提高系统性能。此外，我们还展示了基于可扩展的深度强化学习的方法在最小化计算卸载用例的能耗方面的有效性。最后，确定了接口标准化、联邦学习和迁移学习方面的未解决问题。