Spatio-temporal fusion for small-scale primary detection in cognitive radio networks

Authors:
Alexander W. Min;Xinyu Zhang;Kang G. Shin
Affiliations:
Real-Time Computing Laboratory, Dept. of EECS, The University of Michigan, Ann Arbor, MI;Real-Time Computing Laboratory, Dept. of EECS, The University of Michigan, Ann Arbor, MI;Real-Time Computing Laboratory, Dept. of EECS, The University of Michigan, Ann Arbor, MI
Venue:
INFOCOM'10 Proceedings of the 29th conference on Information communications
Year:
2010

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Abstract

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.