Paper Info
Title
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.
Year
DOI
Venue
2021
10.1109/MNET.011.2000656
IEEE Network
Keywords
DocType
Volume
harmonizing artificial intelligence,radio access networks,AI-driven fog RAN paradigm,AI capsules,network functions,network data collection,centralized AI brain,wireless-oriented autoAI platform,real-time AI-based F-RAN optimization,AI-driven F-RAN paradigm,deep-reinforcement-learning-based method
Journal
35
Issue
ISSN
Citations 
4
0890-8044
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yaohua Sun100.68
Mugen Peng22779200.37
Yijing Ren300.34
Li Chen400.34
Li Yu500.68
Shiqiang Suo620.73