Asymptotic performance analysis for common data delivery in cognitive radio networks

This paper investigates the asymptotic throughput performance of common data delivery in cognitive radio networks (CRNs). Three schemes, named unicast, conventional multicast (CM) and Multiple Description Coding Multicast (MDCM), are compared by utilizing extreme value theory. The distributions of effective signal-to-noise-ratio (SNR) for unicast and CM belong to Domain of Attraction of Gumbel distribution for maxima and Domain of Attraction of Weibull distribution for minima, respectively. Based on asymptotic distribution of a central order statistic, the asymptotic performance of MDCM is then investigated. We first formulate it as an optimization problem, and then transform it into an equivalent one and solve it efficiently. By both theoretic derivation and simulation analysis, it is found that when the multicast group size grows, the per-user throughput of both unicast and CM decays to zero rapidly, while that of MDCM approaches to a positive constant, which implies that MDCM is a more promising scheme for common information delivery in CRNs considering that the number of users is typically large in CRNs.