A novel interference alignment scheme based on antenna selection in cognitive radio networks

Interference alignment (IA) is a promising technique that can eliminate the interferences in wireless networks effectively, and has been applied to cognitive radio (CR). However, the quality of desired signal may be poor when the interferences are aligned in the direction similar to that of the desired signal. Thus, we propose a novel IA scheme based on antenna selection to improve the performance of CR networks. In the proposed scheme, multiple antennas are equipped at each secondary receiver, and we choose some of them that have the optimal channel coefficients according to a certain objective function. Furthermore, we also consider the condition of imperfect channel state information (CSI), and an efficient antenna selection IA algorithm based on discrete stochastic optimization is proposed. Simulation results show that the proposed schemes can improve the performance of IA-based CR networks significantly.