On the throughput of cognitive radio networks using eigenvalue-based cooperative spectrum sensing under complex Nakagami-m fading

In this paper, we study the impact of eigenvalue-based spectrum sensing techniques in the achievable throughput of the secondary cognitive radio network. For this analysis, the channel between the primary and secondary users is modeled as a complex Nakagami-m distribution, in order to investigate the effects of not only the channel envelope, but also the novel non-uniform phase distribution in the detection process. Simulated scenarios are run using the energy detection, the maximum eigenvalue detection, the maximum-minimum eigenvalue detection and the generalized likelihood ratio test. The achievable network throughput is then found as a function of the number of samples, where the channel envelope and phase parameters are arbitrarily varied to investigate their effects on the spectrum sensing and, consequently, on the secondary network throughput itself.