Artificial Intelligence Powered Mobile Networks: From Cognition to Decision

Mobile networks (MNs) are anticipated to provide unprecedented opportunities to enable a new world of connected experiences and radically shift the way people interact with everything. MNs are becoming more and more complex, driven by ever increasing complicated configuration issues and blossoming n...

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Bibliographic Details
Published in:IEEE network 2022-05, Vol.36 (3), p.136-144
Main Authors: Luo, Guiyang, Yuan, Quan, Li, Jinglin, Wang, Shangguang, Yang, Fangchun
Format: Article
Language:English
Subjects:
Artificial intelligence
Cognition
Cognition & reasoning
Complexity
Complexity theory
Computers
Decisions
Deep learning
Maintenance engineering
Optimization
Quality of service
System dynamics
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Summary:Mobile networks (MNs) are anticipated to provide unprecedented opportunities to enable a new world of connected experiences and radically shift the way people interact with everything. MNs are becoming more and more complex, driven by ever increasing complicated configuration issues and blossoming new service requirements. This complexity poses significant challenges in deployment, management, operation, optimization, and maintenance, since they require complete understanding and cognition of MNs. Artificial intelligence (AI), which deals with the simulation of intelligent behavior in computers, has demonstrated enormous success in many application domains, suggesting its potential in cognizing the state of an MN and making intelligent decisions. In this article, we first propose an AI-powered MN architecture and discuss challenges in terms of cognition complexity, decisions with high-dimensional action space, and self-adaptation to system dynamics. Then potential solutions associated with AI are discussed. Finally, we propose a deep learning approach that directly maps the state of an MN to perceived QoS, integrating cognition with the decision. Our proposed approach helps operators to make more intelligent decisions to guarantee QoS. Meanwhile, the effectiveness and advantages of our proposed approach are demonstrated on a real-world dataset involving 31,261 users over 77 stations within 5 days.
ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.013.2100087