Harmonizing Artificial Intelligence with Radio Access Networks: Advances, Case Study, and Open Issues

Abstract

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.