摘要
针对大规模多输入多输出(Multiple-Input Multiple-Output,MIMO)系统中信道估计训练开销大、计算复杂度高的问题,利用信道矩阵的低秩特性,提出了基于低秩矩阵近似的信道估计方法。采用非凸的γ-范数近似信道矩阵秩函数,提高了信道矩阵秩近似的精确性。利用交替方向乘子法求解信道矩阵,选择较少的信道参数降低训练开销。变量迭代更新过程中采用梯度下降法避免了求逆运算,有效降低计算复杂度。通过选择较小的迭代步长,提高信道估计精度。仿真结果表明,所提算法相较于其他低秩矩阵恢复算法具有更好的信道估计性能。
As the traditional channel estimation methods cost large training overhead and hold high computational complexity in massive multiple-input multiple-output(MIMO)systems,a channel estimation method based on low rank matrix approximation is proposed by utilizing the low-rank property of the channel matrix.The rank function of the channel matrix is expressed as the non-convexγ-norm to increase the accuracy of the rank approximation of the channel matrix,and the alternating direction algorithm of multipliers is adopted to solve the channel matrix,which can reduce the training overhead greatly with fewer channel parameters.Moreover,during the iteration,the gradient descent method is adopted to avoid the matrix inverse operation.This effectively reduces the computational complexity.The accuracy of channel estimation is also improved by selecting a smaller iteration step.The simulation results show that the proposed algorithm performs better in channel estimation than other low-rank matrix recovery algorithms.
作者
张琳
黄学军
ZHANG Lin;HUANG Xuejun(College of Telecommunications&Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《南京邮电大学学报（自然科学版）》
北大核心
2022年第1期30-36,共7页
Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition
基金
国家自然科学基金(61427801)资助项目。
关键词
大规模MIMO
低秩矩阵近似
信道估计
训练开销
massive multiple-input multiple-output(MIMO)
low-rank matrix approximation
channel estimation
training overhead
作者简介
张琳,女,硕士研究生;通信作者:黄学军,男,博士,副教授,huangxj@njupt.edu.cn。
