认知网络中基于三角分解的干扰对齐算法

针对多个主用户、多个次用户的认知多输入多输出网络,由次用户单方面消除主次间的干扰,带来次用户性能严重损失的问题,提出一种联合主次用户信道矩阵进行三角分解的干扰对齐算法。首先,根据各个用户的信道质量分别对主用户系统和次用户系统的信道矩阵进行排序;然后,结合主用户网络和次用户网络的信道矩阵进行三角分解;最后,通过最小均方误差算法来验证所提算法的可行性。仿真实验表明,所提算法能有效地提高次用户自由度上限以及次用户网络和主用户网络的系统容量、平均能量效率及抑制干扰的能力。

Interference alignment algorithm based on trigonometric decomposition in cognitive networks

In multiple primary and multiple secondary users’ cognitive multiple-input multiple-output networks, the secondary users unilaterally eliminate the interference between primary users and secondary users, and bring about serious loss of secondary users’ performance. To solve this problem, a joint primary users and secondary users channel matrix is proposed for triangular decomposed interference alignment. Firstly, the channel matrix of the primary users system and the secondary users system is sorted according to the user channel quality. Then, the primary and secondary user channel matrix is triangularly decomposed. Finally, the feasibility of the proposed algorithm is verified by the least mean square error algorithm. The simulation results show that the proposed algorithm can effectively improve the upper limit of secondary users’ degrees of freedom, and the system capacity, the average energy efficiency and the ability to suppress interference of secondary users network and the primary users network.