Dynamic Distribution-Free Spectrum Sensing

Cyclostationary detector (CSD) is one of the spectrum sensing techniques used in cognitive radio. Although it has a superior performance in Gaussian environments, its performance severely deteriorates in non-Gaussian environments. This paper proposes a novel dynamic CSD-based algorithm that jointly utilizes the generalized seasonal block bootstrap (GSBB) re-sampling techniques and the cyclic mismatch property to improve the performance of the CSD detector in non-Gaussian environments. The novelty of this algorithm lies in the fact that it does not require prior knowledge about the noise distribution nor does it require noise-only samples to set the detection threshold. Simulation results show that the proposed algorithm maintains the required probability of false alarm at 10% under Gaussian and non-Gaussian noise, unlike its counterparts which increase the false alarm probability up to 38% under non-Gaussian noise.