MIMO Interference Alignment For Cognitive Radio Network

In this paper, guaranteeing that the authorized users will not be seriously interfered by the cognitive users, we adopt the cognitive radio technology and use the authorized users’ band to do research. It will focus on the adoption of the cognitive radio in MIMO system and realize the cognitive interference alignment and elimination from two different perspectives.Firstly, it will introduce the research background and the key technologies used in the study. Secondly, it will analyze the cognitive radio network environment and contrast the existing interference alignment techniques. Finally, the active interference cancellation techniques used in this article will be put forward based on the above.Supposing that the transmission bandwidth of the CU is wider than that of the PU, the spectrum of the PU is called victim band. The unequal transmission bandwidth of two users caused the different sampling rates. We consider that these two interference sources can be eliminated by alignment, one of which is the spatial interference from different antennas of the CU, and the other is the inter-carrier interference (ICI) from the un-victim band of CU to the victim band of PU. We propose the active interference cancellation techniques. It means that an active antenna of the cognitive user is used to align with the spatial interference from the other antennas of the cognitive user and ICI so that the interference to the PU can be nullified. In the simulation, we design the cognitive user as MIMO OFDM systems and apply different receiver detection methods to simulate the BER rate of the cognitive users’ receivers.The second technique in this article is the Opportunistic Spatial Orthogonalization (OSO) interference cancellation, which is even though the primary users occupy all the frequency bands all the time, the existence of secondary users are still able toincrease the sum throughput of the whole system. Its key idea is to exploit the spatial dimensions to orthogonalize users and hence minimize the mutual interference. First of all, we introduce the OSO when there is only one waiting secondary users in the SIMO, and we further extend it to the case when there are many waiting secondary users, then we apply the opportunity space orthogonal planning to MIMO systems. Finally, we simulate respectively the system throughput when the different interference threshold values of these two systems vary. It verifies the advantages and significance of the opportunity space orthogonal plan in the cognitive MIMO systems.