Adaptive Counting Rule for Cooperative Spectrum Sensing Under Correlated Environments

Spectrum sensing is the cognitive radio mechanism that enables spectrum awareness. It has been shown in the literature that spectrum sensing performance can be greatly improved through the use of cooperative sensing schemes.

This paper considers and proposes a data fusion based cooperative spectrum sensing scheme based on data fusion, where an adaptive counting rule is used to implement the data fusion. The proposed scheme is evaluated against other common counting rules (e.g. 1-out-of-c and c-out-of-c) found in the literature and the optimum counting rule, while under different correlation conditions. The impact of correlation on the performance of the considered counting rules is then studied.

It is concluded that the proposed adaptive counting rule detection performance reaches in some cases the one of the optimum counting rule, and therefore it adapts to the correlation conditions which the network nodes are experiencing.