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有限反馈干扰对齐对信道矩阵进行量化从而降低反馈所需要的信息量,但经典有限反馈干扰对齐算法只考虑对信道状态矩阵的量化,且多限于理论分析.针对分布式干扰对齐技术中预编码矩阵反馈数据量要比信道矩阵反馈大得多的情况,从降低分布式干扰对齐技术预编码矩阵反馈对反馈信道的影响出发,提出一种基于格拉斯曼码本的分布式干扰对齐算法,基于格拉斯曼码本对预编码矩阵和重组矩阵进行量化,并以最小化干扰泄漏为目标进行迭代优化.仿真实验结果表明,当信噪比小于或等于15 dB,且迭代次数小于或等于10次时,该算法能在系统性能接近理想反馈条件的同时有效降低反馈信息量.
Limited feedback interference alignment quantifies the channel matrix to reduce the amount of information required for feedback, but classical finite feedback interference alignment algorithm considers only the quantization of the channel state matrix, and is mostly limited to theoretical analysis.For the purpose of distributed interference alignment technology precoding matrix In order to reduce the influence of precoding matrix feedback of distributed interference alignment on the feedback channel, we propose a distributed interference alignment algorithm based on Grasmell codebook, Siman codebook to pre-code matrix and recombination matrix to quantify and minimize the interference leakage for the purpose of iterative optimization.The simulation results show that when the SNR is less than or equal to 15 dB, and the number of iterations is less than or equal to 10 times This algorithm can effectively reduce the amount of feedback information while the system performance approaches the ideal feedback condition.