摘要： 针对基于导频污染的大规模多输入多输出系统，提出一种空间交替广义期望最大化（SAGE）迭代的信道估计算法。将发送和接收的导频符号形成完备的数据空间集，利用基于导频的最小均方误差估计器初始化信道参数，通过导频符号和软信息迭代改进并更新初始估计数据。实验结果表明，该算法通过一次SAGE迭代即可实现精度收敛，且在高信噪比及导频污染的情况下相比基于导频的估计器具有更高的估计精度及频谱效率。

关键词: 空间交替广义期望最大化, 多输入多输出, 信道估计, 迭代, 导频污染

Abstract: Aiming at the massive Multiple Input Multiple Output(MIMO) systems based on pilot pollution,a channel estimation algorithm with Space-Alternating Generalized Expectation-Maximization(SAGE) iteration is proposed.A complete data space set is formed with the transmitted and received pilot symbols.The pilot-based Minimum Mean Square Error(MMSE) estimator is used to initialize the channel parameters.Pilot symbols and soft information iteration is used to improve and update initial estimated data.Experimental results show that the algorithm can achieve precision convergence through a SAGE iteration,and it has higher estimation accuracy and spectral efficiency than the existing pilot-based estimator in the case of high Signal Noise Ratio(SNR) and pilot pollution.

Key words: Space-Alternating Generalized Expectation-Maximization(SAGE), Multiple Input Multiple Output(MIMO), channel estimation, iteration, pilot pollution

中图分类号: 

• TN929.53