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Spatio-Temporal Fusion for Small-scale Primary Detection in Cognitive Radio Networks

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3 Author(s)
Min, A.W. ; Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, USA ; Xinyu Zhang ; Shin, K.G.

In cognitive radio networks (CRNs), detecting small-scale primary devices---such as wireless microphones (WMs)---is a challenging, but very important, problem that has not yet been addressed well. We identify the data-fusion range as a key factor that enables effective cooperative sensing for detection of small-scale primary devices. In particular, we derive a closed-form expression for the optimal data-fusion range that minimizes the average detection delay. We also observe that the sensing performance is sensitive to the accuracy in estimating the primary's location and transmit-power. Based on these observations, we propose an efficient sensing framework, called DeLOC, that iteratively performs location/transmit-power estimation and dynamic sensor selection for cooperative sensing. Our extensive simulation results in a realistic CRN environment show that DeLOC achieves near-optimal detection performance, while meeting the detection requirements specified in the IEEE 802.22 standard draft.

Published in:

INFOCOM, 2010 Proceedings IEEE

Date of Conference:

14-19 March 2010