Title
Artificial Intelligence Powered Mobile Networks: From Cognition to Decision
Abstract
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.
Year
DOI
Venue
2022
10.1109/MNET.013.2100087
IEEE Network
Keywords
DocType
Volume
artificial intelligence powered mobile networks,optimization,intelligent decisions,cognition complexity,high-dimensional action space,AI-powered mobile networks architecture,QoS
Journal
36
Issue
ISSN
Citations 
3
0890-8044
0
PageRank 
References 
Authors
0.34
9
5
Name
Order
Citations
PageRank
Guiyang Luo151.75
Quan Yuan25511.07
Jinglin Li315030.39
Shangguang Wang481688.84
Fangchun Yang5108290.49