Sensing-Transmission Edifice Using Bayesian Nonparametric Traffic Clustering in Cognitive Radio Networks

In cognitive radio networks, the main objective of spectrum sensing is to exploit spectrum holes left by the primary users (PUs). Different PUs´ traffic patterns might provide different opportunities for second user (SU) spectrum access. In this paper, we identify the PUs´ traffic patterns and then maximize SU transmission accordingly. First a theoretical framework is developed to cluster PU traffic patterns based on a Bayesian nonparametric inference model, in which the number of traffic types is unknown. Second, in order to exploit the spectrum holes, we study a sensing-transmission structure to optimize the SU transmission strategy. Specifically, we exploit the short and long transmission opportunities based on the PU traffic pattern and channel idle time distribution. Finally, we propose a threshold-based sensing-transmission method that optimizes the SU utility, while protecting PU transmissions. Both sensing and transmission errors are considered for perfect sensing with/without acknowledgement-based transmission and imperfect sensing, respectively. From the simulation results, we show that the proposed technique outperforms the nonparametric mean shift clustering algorithm. Furthermore, we utilize these clustering results to optimize the SU´s transmission strategy with perfect and imperfect sensing. We compare our proposed technique with the probabilistic sensing-transmission structure and show the performance gain in terms of throughput.