Robust Spectrum Sensing Using Moving Blocks Energy Detector with Bootstrap

The spectrum sensing function, is one of the primary requirements to ensure efficient and reliable cognitive radio network since it determines whether to allow an unlicensed or secondary user to use certain spectrum resources that are not being used by its owner or primary users. In this paper, we combine the goodness of fit test and bootstrap resampling techniques to address spectrum sensing for the case of a small samples size, thus a faster-sensing step, and where the asymptotic methods do not perform well. It is shown that the proposed method, in the case of a single secondary user, outperforms the energy detection method and the technique based only on the goodness of fit test.

Bootstrap approach for cognitive radio

In this paper, we consider the primary user (PU) detection problem in interweave cognitive networks, in the presence of an impulse noise. Adopting Middleton's Class A distribution for this latter, and a sequential probability ratio test for the PU detection, closed form expressions are derived for the per-sample log likelihood ratio, as well as the average sample numbers under binary hypothesis testing.

Toufik Mouchini

Papers

Bootstrap approach for cognitive radio

Sequential detection under impulsive Noise