Nonparametric Bayesian identification of primary users' payloads in cognitive radio networks

In cognitive radio networks, a secondary user needs to estimate the primary users' traffic patterns so as to optimize its transmission strategy. In this paper, we propose a nonparametric Bayesian method for identifying traffic applications, since the traffic applications have their own distinctive patterns. In the proposed algorithm, the collapsed Gibbs sampler is applied to cluster the traffic applications using the infinite Gaussian mixture model over the feature space of the packet length, the packet inter-arrival time, and the variance of packet lengths. We analyze the effectiveness of our proposed technique by extensive simulation using the measured data obtained from the WiMax networks.