A Bayesian model of the aggregate interference power in cognitive radio networks

Abstract

Interference is one of the critical factors that affects the performance of cognitive radio networks. In these networks, secondary users are allowed to use the primary user channel with the condition that they cause no interference to it. Interference power received at the primary user is impacted by a number of parameters, including nodes transmit powers, distance between the primary user and the other nodes, path loss, and shadowing. A number of techniques have been proposed to model the interference power. However, these techniques do not consider the uncertainty associated with these parameters. Therefore, a model that deals with the uncertainty affecting the aggregate interference power is needed. In this paper, a Bayesian model is proposed to probabilistically describe how a number of factors affect the aggregate power of interference.