Research Article
Binary is good: A binary inference framework for primary user separation in cognitive radio networks
@INPROCEEDINGS{10.4108/ICST.CROWNCOM2010.9248, author={Huy Nguyen and Rong Zheng and Zhu Han}, title={Binary is good: A binary inference framework for primary user separation in cognitive radio networks}, proceedings={5th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications}, publisher={IEEE}, proceedings_a={CROWNCOM}, year={2010}, month={9}, keywords={Accuracy Cognitive radio Inference algorithms Monitoring Noise Object recognition Sensors}, doi={10.4108/ICST.CROWNCOM2010.9248} }- Huy Nguyen
Rong Zheng
Zhu Han
Year: 2010
Binary is good: A binary inference framework for primary user separation in cognitive radio networks
CROWNCOM
IEEE
DOI: 10.4108/ICST.CROWNCOM2010.9248
Abstract
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.
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