Optimization of Fusion Center Parameters With Threshold Selection in Multiple Antenna and Censoring-Based Cognitive Radio Network

Cognitive radio technology is a potential contender to fulfil the demand of spectrum/bandwidth for a large number of connected devices of the next-generation internet of things (IoT) network. Spectrum sensing is the crucial step of cognitive radio, and its performance is affected by the selection of sensing threshold and cooperation among multiple cognitive users (CUs). The accuracy of spectrum sensing results is a major concern in cognitive radio networks (CRN). Therefore, in this paper, we have minimized the Bayes risk which deals with the spectrum sensing error. In general, the
${k}$
-out-of-
${M}$
fusion rule is employed at fusion center (FC) in the cooperative CRN and optimal
${k}$
and
${M}$
with selection of spectrum sensing threshold results in minimum Bayes risk. Further, we have derived the expression for optimal value of CUs (
${k}$
and
${M}^{{r}}$
) in
${k}$
-out-of-
${M}^{{r}}$
rule in the cooperative spectrum sensing (CSS) at all signal-to-noise ratio (SNR) while employing different threshold selection approaches to minimize the Bayes risk at FC. The considered scenario employs multiple antennas at each CU where each CU report to the FC over the perfect/imperfect reporting channel with non-censoring (
${M}^{{r}}={M}$
) and censoring (
${M}^{{r}}={M}^{{c}}$
) approaches. Further, we have also validated the proposed results with recently reported literature and have shown that the existing expressions are the special case of the proposed generalized expressions.