Cognitive Radio Network Interference Modeling With Shadowing Effect Via Scaled Student~s T Distribution

In recently developed Cognitive Radio Network (CRN), the spectrum sharing leads to many uncertainties associated with the aggregate interference in the network. It is highly desired to build an interference model for such cognitive radio networks to express such uncertainties to quantify the effect of the interference on the primary network. However, existing interference models have not account for lognormal shadowing due to the difficulty to estimate the entire lognormal sum distribution. In this paper, the authors propose to utilize the Scaled Student's t distribution to approximate the shadowing effect and improve existing interference models in CRN.