Cyclostationary spectrum sensing for Cognitive Radio and multiantenna systems

Abstract

In this paper it is shown that cyclostationary spectrum sensing for Cognitive Radio networks, applying multiple cyclic frequencies for single user detection can be interpreted in terms of optimal incoherent diversity addition for "virtual diversity branches" or SIMO radar. This approach allows proposing, by analogy to diversity combining, suboptimal algorithms which can provide near optimal characteristics for the Neyman-Pearson Test (NPT). The analysis is based on the Generalized Gaussian (Klovsky-Middleton) Channel Model, which allows obtaining the above mention NPT characteristics: probability of misdetection (PM) and probability of false alarm (Pfa) in the most general way. Some quasi-optimum algorithms such as energetic receiver and selection addition algorithm are analyzed and their comparison with the noise immunity properties (Receiver Operational Characteristics-ROC) of the optimum approach is provided as well.