Multiuser-diversity-based interference alignment in cognitive radio networks

Abstract As a promising interference management technique, interference alignment (IA) has been applied to cognitive radio (CR) networks. However, the received signal-to-interference-plus-noise ratio (SINR) may decrease dramatically under some channel conditions in IA-based CR networks, and this will reduce the quality of service (QoS) of primary users (PUs). In this paper, we study the problem of SINR decrease and propose a multiuser-diversity-based IA scheme to make it more practical to be applied to CR networks. Since the number of secondary users (SUs) is changing dynamically in practical CR networks, we present two schemes targeted at two different scenarios. In the first scenario with a large number of SUs, the IA network cannot accommodate all the PUs and SUs simultaneously with perfect elimination of interferences. The corresponding scheme is to select those SUs, which can maximally improve the QoS of PUs, to access to the spectrum by forming an IA network with the PUs. Thus the performance of PUs can be significantly improved. To further ensure the interest of SUs, the scheme is revised and a tradeoff is made between the PUs and SUs. In the second scenario with a smaller number of SUs, the IA network can accommodate all the users simultaneously without mutual interference. User selection and antenna selection strategies are adaptively employed to guarantee the performance of PUs. Furthermore, fairness among SUs is also investigated. Simulation results are presented to show the effectiveness of the proposed schemes for CR networks.