Relevance of Dirichlet process mixtures for modeling interferences in underlay cognitive radio

Abstract

In the field of underlay cognitive radio communications, the signal transmitted by the secondary user is disturbed by incoming signals from primary users. Thus, it is necessary to compensate for this secondary-link degradation at the receiver level. In this paper we use Dirichlet process mixtures (DPM) to relax a priori assumptions on the characteristics of the primary user-induced interference. DPM allow us to model the probability density function of the interference. The latter is estimated jointly with the symbols and the channel of the secondary link by using marginalized particle filtering. Our approach makes it possible to improve the symbol error rate compared with an algorithm that simply models the interference as a Gaussian noise.