Interference Alignment In Cognitive Network

With the rapid increase in the number of users, service types and demands are getting diverse. Communication technology has made great progress, mobile communication has experienced 2G, 3G, and 4G technology. In order to ensure that the signal does not interfere with each other, different systems occupy the different frequency bands, which make the spectrum resources become increasingly scarce. On the one hand, the spectral resources close to being allocated exhausted, on the other hand, the utilization of spectrum which has been allocated is still at a low level, cognitive radio network is proposed in this context. The cognitive wireless network is flexible and adaptable to the specific wireless communication system, which can effectively solve the resource management and optimization problems in heterogeneous networks and improve the efficiency of spectrum utilization.In this paper, we focus on applying the techniques of interference alignment and antenna selection to cognitive wireless network, and providing an effective, low complexity method to improving system capacity in cognitive networks. The main contents of this paper are listed as below,1. The interference alignment technique is used to optimize the code and decode matrixes of the primary users and secondary users respectively.The primary users use the maximum signal to interference and noise ratio(SINR) algorithm to obtain a certain degree of freedom while obtaining a certain coherence gain. The secondary users use the cognitive interference alignment algorithm to obtain considerable degrees of freedom while eliminating interference to the primary users.2. Use antenna selection techniques to select the most efficient subset of available receive antennas to reduce the RF link configuration. The usage of antenna selection techniques can select the optimal antenna configuration for the secondary users. With the help of antenna selection techniques, we can increase capacity without adding extra bandwidth significantly, and seek a balance point between performance and achievability.The purpose of this paper is to improve the capacity of the system.Improve the transmission rate of the primary users with a low complexity algorithm without reducing the transmission rate of the secondary users.