This study addresses the optimal spectrum sensing detection based on the complete or partial side-information on the signal and noise statistics. The use of the generalised-likelihood ratio test (GLRT) involves maximum-likelihood (ML) estimation of the nuisances. ML estimation of the unknowns is especially challenging for wideband cognitive radio because closed-form solutions are often not available. Based on the equivalence between the wideband regime and the low-signal-to-noise ratio regime, this study provides a general kernel framework for GLRT spectrum sensing. It is shown that any GLRT detector exclusively depends on the projection of the sample covariance matrix of the data onto a given underlying kernel that reflects the available side-information in the problem. The kernels in several scenarios of interest are derived, including the widespread single and multi-frequency channelisation cases. Theoretical interpretations and numerical results show the trade-off between detection performance and the degree of side-information on the most informative statistics for detection, that is, the modulation format and spectrum distribution of the primary users.

Peer Reviewed