Channel allocation under uncertain primary users for delay sensitive secondary users

For reducing the channel switching frequency due to uncertain and frequent primary users' interruptions, we design a nonparametric channel allocation algorithm which can effectively manage the spectrum resources in distributed cognitive radio networks and serve for delay-sensitive data traffics. In the proposed algorithm, an infinite Gaussian mixture model is used to get the posterior decision from the feature spaces of the channel idle duration and the primary user's frequency channel, while the collapsed Gibbs sampling is applied for fast convergence. Experimental and simulation results demonstrate that the proposed channel allocation algorithm outperforms the random channel allocation method and the idle probability based channel allocation algorithm in terms of throughput and packet drops.