By Topic

Binary Inference for Primary User Separation in Cognitive Radio Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Huy Nguyen ; Dept. of Comput. Sci., Univ. of Houston, Houston, TX, USA ; Guanbo Zheng ; Rong Zheng ; Zhu Han

Spectrum sensing problem, which focuses on detecting the presence of primary users (PUs) in the cognitive radio (CR) network receives much attention recently. In this paper, we introduce the PU separation problem, which concerns with the issue of distinguishing and characterizing the activities of PUs in the context of collaborative spectrum sensing and monitor selection. Observations of secondary users (SUs) are modeled as boolean OR mixtures of underlying binary PU sources. We devise a binary inference algorithm for PU separation. With binary inference, not only PU-SU relationship are revealed, but PUs' transmission statistics and activities at each time slot can also be inferred. Simulation results show that without any prior knowledge regarding PUs' activities, the algorithm achieves high inference accuracy even in the presence of noisy measurements.

Published in:

Wireless Communications, IEEE Transactions on  (Volume:12 ,  Issue: 4 )