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Proceedings Paper

Machine learning based Intelligent cognitive network using fog computing
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Paper Abstract

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

Paper Details

Date Published: 5 May 2017
PDF: 9 pages
Proc. SPIE 10196, Sensors and Systems for Space Applications X, 101960G (5 May 2017); doi: 10.1117/12.2266563
Show Author Affiliations
Jingyang Lu, Intelligent Fusion Technology, Inc. (United States)
Lun Li, Intelligent Fusion Technology, Inc. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Dan Shen, Intelligent Fusion Technology, Inc. (United States)
Khanh Pham, Air Force Research Lab. (United States)
Erik Blasch, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 10196:
Sensors and Systems for Space Applications X
Khanh D. Pham; Genshe Chen, Editor(s)

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