Single and multi‐frequency wideband spectrum sensing with side‐information
Josep Font‐Segura, G. Vazquez, Jaume Riba · IET Signal Processing · 2014
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.