NDLI: Predictive behavior classification for cognitive radio: Introduction and preliminary results

Content Provider	IEEE Xplore Digital Library
Author	DePoy, D. Bose, T.
Copyright Year	2012
Description	Author affiliation: Wireless@VT, Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA (DePoy, D.; Bose, T.)
Abstract	Cognitive Radio systems rely heavily on artificial intelligence capabilities to perform a variety of tasks. Sharing spectrum resources more efficiently, self organization, and interference mitigation are just a few examples. For many CR applications, a primary goal is to decentralize and distribute network functions among participant nodes. As a consequence, any given node in a CR network may be required to coordinate with not only its peers, but also with a number of unknown transmitters. Thus, it is desirable that individual nodes be capable of predicting future states of non-peer transmitters in order to better optimize their own operation. In this paper we introduce methods for identifying cognitive behavior in an unknown transmitter and predicting likely future states based on physical spectrum observations. We discuss the problem in the context of our Universal DSA Network Simulation (UDNS) and present two behavior classification algorithms used to this end.
Starting Page	280
Ending Page	284
File Size	188563
Page Count	5
File Format	PDF
ISBN	9781467329767
ISSN	21665419
e-ISBN	9781936968558
DOI	10.4108/icst.crowncom.2012.248518
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-18
Publisher Place	Sweden
Access Restriction	Subscribed
Rights Holder	ICST
Subject Keyword	Training AODE Accuracy Naive Bayes Radio transmitters Channel estimation Prediction algorithms Coginitive Radio Classification algorithms Niobium Dynamic Spectrum Access Behavior Classification
Content Type	Text
Resource Type	Article

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