TY - GEN
T1 - Predictive behavior classification for cognitive radio
T2 - 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Ntworks and Communications, CROWNCOM 2012
AU - DePoy, Daniel
AU - Bose, Tamal
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - AODE
KW - Behavior Classification
KW - Coginitive Radio
KW - Dynamic Spectrum Access
KW - Naive Bayes
UR - http://www.scopus.com/inward/record.url?scp=84869480417&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869480417&partnerID=8YFLogxK
U2 - 10.4108/icst.crowncom.2012.248518
DO - 10.4108/icst.crowncom.2012.248518
M3 - Conference contribution
AN - SCOPUS:84869480417
SN - 9781936968558
T3 - Proceedings of the 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2012
SP - 280
EP - 284
BT - Proceedings of the 2012 7th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2012
Y2 - 18 June 2012 through 20 June 2012
ER -