Efficient learning of statistical primary patterns via Bayesian network Conference Paper

abstract

• 2015 IEEE. In cognitive radio (CR) technology, the trend of sensing is no longer to only detect the presence of active primary users. A large number of applications demand for primary user behavior correlation in spatial, temporal, and frequency domains. To satisfy such requirements, we study the statistical relationship of primary users by introducing a Bayesian network (BN) based framework. How to learn such a BN structure is a long standing issue, not fully understood even in the statistical learning community. To solve such an issue in CR, this paper proposes a BN structure learning scheme which incurs significantly lower computational complexity compared with previous ones. Thus, with this scheme, cognitive users could efficiently understand the statistical pattern of primary networks.

name of conference

• 2015 IEEE International Conference on Communications (ICC)

authors

• Sang, Huiyan

published proceedings

• 2012 IEEE International Conference on Communications (ICC)

author list (cited authors)

• Han, W., Sang, H., Sheng, M., Li, J., & Cui, S.

citation count

• 1

complete list of authors

• Han, Weijia||Sang, Huiyan||Sheng, Min||Li, Jiandong||Cui, Shuguang

publication date

• January 2015

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• 2013 National Conference on Communications, NCC 2013  Journal

keywords

• Basic Behavioral And Social Science
• Behavioral And Social Science
• Neurosciences

Digital Object Identifier (DOI)

• 10.1109/icc.2015.7249094

International Standard Book Number (ISBN) 13

• 9781467364324

start page

• 4871

end page

• 4876

volume

• 2015-September

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Ficc.2015.7249094