Paper Info
Title
Exploring Qualitative Probabilistic Networks for knowledge modeling in Cognitive Wireless Networks
Abstract
The suitability of using Qualitative Probabilistic Networks (QPN) for knowledge modeling and inference in Cognitive Wireless Networks is studied in this paper. This can be considered as a light weight approach compared to the complexity associated with the use of Bayesian Networks. This study brings out the advantages and issues involved in using QPN for modeling the dynamic behavior of wireless networks. Application and limitations of using QPN is illustrated with the modeling of a cognitive radio link and subsequently its performance while driving a link adaptation. The same methodology is extendable to model network layer behaviors as well.
Year
DOI
Venue
2013
10.1109/IWCMC.2013.6583823
IWCMC
Keywords
Field
DocType
radio links,qualitative probabilistic network,cognitive wireless network,cognitive engine,cognitive radio link,dynamic behavior modeling,cognitive radio,light weight approach,network layer behavior model,cognitive cross layer optimization,cognitive networks,qpn,knowledge modeling,qualitative probabilstic networks,knowledge inference,probability,sensors,cognition,interference,optimization,signal to noise ratio
Link adaptation,Wireless network,Computer science,Inference,Bayesian network,Artificial intelligence,Cognitive model,Machine learning,Knowledge modeling,Cognitive network,Cognitive radio
Conference
ISSN
ISBN
Citations 
2376-6492
978-1-4673-2479-3
0
PageRank 
References 
Authors
0.34
5
1
Name
Order
Citations
PageRank
Balamuralidhar P1105.25