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CORR
2010
Springer

Binary is Good: A Binary Inference Framework for Primary User Separation in Cognitive Radio Networks

8 years 8 months ago
Binary is Good: A Binary Inference Framework for Primary User Separation in Cognitive Radio Networks
Primary users (PU) separation concerns with the issues of distinguishing and characterizing primary users in cognitive radio (CR) networks. We argue the need for PU separation in the context of collaborative spectrum sensing and monitor selection. In this paper, we model the observations of monitors as boolean OR mixtures of underlying binary latency sources for PUs, and devise a novel binary inference algorithm for PU separation. Simulation results show that without prior knowledge regarding PUs' activities, the algorithm achieves high inference accuracy. An interesting implication of the proposed algorithm is the ability to represent n independent binary sources via (correlated) binary vectors of logarithmic length.
Huy Anh Nguyen, Rong Zheng, Zhu Han
Added 22 Mar 2011
Updated 22 Mar 2011
Type Journal
Year 2010
Where CORR
Authors Huy Anh Nguyen, Rong Zheng, Zhu Han
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