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中文摘要：
针对多个主用户和多个次用户的多输入多输出（MIMO）认知网络,本文给出了一种不依赖信道互惠性和不需要前后向链路交替式迭代的干扰对齐方法。对于次用户,首先,通过对其进行编码,建立了消除主次用户间相互干扰后的等效模型。然后,在等效模型的基础上,以最大化总容量为目标函数设计预编码矩阵,并采用基于Grassmann流形上的梯度法对目标函数进行求解得到预编码。最后,在接收端以最大信干噪比准则来设计接收滤波器矩阵。仿真结果显示,在低信噪比时,本文算法与现有典型算法性能相同,而在高信噪比时本文算法性能更优。
英文摘要：
An interference alignment（ IA） algorithm that doesn＇ t require channel reciprocity and alleviates the need to alternate between the forward and reverse link is proposed for multiple primary users（ PUs） and multiple secondary users（ SUs） in multiple input multiple output（ MIMO） cognitive radio network（ CRN）. Firstly,we encode the SUs and set up the equivalent mode after eliminating the interference between the PUs and SUs. Secondly,we establish the cost function of maximizing the total capacity and apply the gradient method on Grassmann manifold to obtain the optimal precoding matrices. Finally,the receiver postprocessing matrices are designed by the criterion of maximizing signal-to-interference-plusnoise. Simulation results show that the same results provided by the proposed algorithm and the existing typical algorithms at low SNRs,but the best results are provided by the proposed algorithm at high SNRs.
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