Paper Info
Title
Artificial-Intelligence-Based Data Analytics for Cognitive Communication in Heterogeneous Wireless Networks
Abstract
Rapidly growing wireless networks are facing spectrum shortages, so how to improve spectrum utilization becomes critical. The rise of artificial intelligence (AI) technologies can provide a more intelligent and effective strategy for realizing cognitive wireless communication to improve spectrum utilization. Therefore, this article uses AI technology for data analytics, and combines cognitive technology to perform dynamic spectrum allocation. In terms of data analytics, AI technology is utilized in both feature extraction and data dimensionality, and the data correlation calculation between users. Then the data analytics results are applied to the spectrum allocation. Combined with deep learning, an AI-driven data-analytics-based spectrum allocation (ADASA) algorithm is proposed. ADASA enables the adaptive adjustment of the allocation parameters according to the network environmental status when allocating spectrum to users. Finally, the simulation results prove that the proposed ADASA algorithm can effectively improve the spectrum utilization in heterogeneous wireless networks.
Year
DOI
Venue
2019
10.1109/MWC.2019.1800351
IEEE Wireless Communications
Keywords
Field
DocType
Artificial intelligence,Data analysis,Resource management,Wireless networks,Correlation,Feature extraction
Resource management,Wireless network,Wireless,Data analysis,Computer science,Curse of dimensionality,Feature extraction,Artificial intelligence,Frequency allocation,Deep learning,Distributed computing
Journal
Volume
Issue
ISSN
26
3
1536-1284
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Kai Lin136546.37
Li Chensi281.85
Daxin Tian320432.49
Ahmed Ghoneim425623.72
M. Shamim Hossain5117183.62
Syed Umar Amin6667.13