Proportional fair scheduling based On primary user traffic patterns for spectrum sensing in cognitive radio networks

In this paper, we propose a novel proportional fair scheduling algorithm for MAC-layer sensing in the cognitive radio networks (CRNs). According to the secondary user (SU) channel state information and primary user (PU) traffic patterns, the SUs are adaptively scheduled to carry out sensing and transmission in different channels. Moreover, we jointly consider multiple important design factors in the proposed algorithm, including network throughput and fairness. Then, the adaptive spectrum sensing problem is formulated as an optimization problem, and the Hungarian algorithm is employed to solve it with polynomial complexity. Simulation results show that the proposed scheme could achieve a good tradeoff between the CRN throughput and the fairness among SUs.