Skew-t copula for dependence modelling of impulsive (α-stable) interference

Impulsive interference is a strong limitation in ultra wide band systems or ad hoc networks. However, many work rely on the assumption of independent interference samples which is in many situations an unrealistic assumption. We propose to model the dependence structure using natural extensions to existing interference models based on parameter copula models. We focus on a particular flexible class of models based on the skewed-t copula family. They allow one to capture interesting dependence features based on extremal concordance such as multivariate generalizations of joint extreme correlation known as tail dependence. In the skew-t copula family this can arise in both homogeneous and heterogeneous forms in the extreme quadrants of the multivariate distribution. Importantly, by considering the skew-t copula it is also amenable to efficient scalability to high dimensions. In a second step, we study the impact of these dependence in the receivers' performance when they are designed assuming i.i.d. signals.