Binary Inference for Primary User Separation in Cognitive Radio Networks



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IEEE Transactions on Wireless Communications


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.



Cognitive radio, spectrum sensing, binary independent component analysis, machine learning, inference channel


Copyright 2013 IEEE Transactions on Wireless Communications. This is a pre-print versoin of a published paper that is available at: Recommended citation: