摘要
针对大规模多入多出(Multiple-Input Multiple-Output,MIMO)系统信道估计需要一定的先验条件及估计精度低的问题,提出一种应用于下行链路的自适应阈值的分段正交匹配追踪的信道估计算法。应用信道矩阵在频域中表现的稀疏特性,采用压缩感知重构算法,结合比例积分微分控制算法思想,每次迭代时,根据当前重构误差自适应调整挑选原子的阈值,一次性高效选择多个与原信号相关性较强的原子;同时引入残差分布特性,通过计算相邻两次迭代的残差关系,动态修正迭代停止阈值,在先验条件未知的情况下,能够更真实地反应迭代状态,从而达到提高信道估计精度的目的。仿真结果表明,在低信噪比下,所提算法与传统的分段匹配正交追踪算法相比,估计性能提高了约0.8 dB并且保持了较低的算法复杂度。
Focused on the issue that channel estimation of massive MIMO systems requires certain prior conditions and have low estimation accuracy,a channel estimation algorithm in downlink based on piecewise orthogonal matching pursuit with adaptive threshold is proposed.Due to the sparsity of channel matrix in frequency domain,compressed sensing reconstruction algorithm can be used.Combining with the method of Proportional-Integral-Derivative(PID)control algorithm,in each iteration,the threshold of selecting atoms is adaptively adjusted according to the current residual,then multiple atoms with strong correlation with the original signal can be selected at one time.Meanwhile,according to the distribution characteristics of residuals,the stopping threshold is dynamically modified by calculating the residual relationship between two adjacent iterations,which can improve the accuracy of channel estimation without the prior condition.The simulation results show that,compared with the StOMP algorithm,the proposed algorithm can improve the channel estimation accuracy while maintaining low complexity.
作者
孙文胜
马天然
SUN Wensheng;MA Tianran(School o f Communication Engineering,Hangzhou Dianzi University,Hangzhou Zhejiang 310018,China)
