基于空间交替广义期望最大化算法的信道参数估计的改进

在大规模多输入多输出(multiple-input multiple-output, MIMO)系统中,空间交替广义期望最大化(space-alternating generalized expectation-maximization, SAGE)算法可以精准有效地估计出信道参数信息,从而被广泛使用。针对SAGE算法两种初始化方法均无法处理信道中多条径的时延相等或接近时,低功率径无法估计出来的情况,提出了一种在估计子径时将功率低于一定阈值的径作为噪声消除掉的SAGE优化方法。在仿真阶段,首先对SAGE算法的两种初始化方式进行了分析与比较;接着对比了改进后的SAGE算法与原算法的性能差异。仿真结果表明,改进后的SAGE算法能够较好地估计出原被噪声淹没的径,提高参数估计精度,并且加快迭代收敛速度。

improvement of channel parameter estimation based on space alternating generalized expectation maximization algorithm

In large-scale multiple input multiple output (MIMO) systems, the channel parameters can be estimated accurately and effectively by space alternating generalized expectation maximization (SAGE) algorithm, so SAGE algorithm is widely used. In view of the fact that the two initialization methods of sage algorithm can''t deal with the situation that the low power path can''t be estimated when the time delays of multiple paths are equal or close, a sage optimization method which takes the path whose power is lower than a certain threshold as the noise elimination when estimating the sub path is proposed in this paper. In the simulation phase, the two initialization algorithms were analyzed and compared, and then the performance difference between the improved sage algorithm and the original algorithm was compared. The results that the improved sage algorithm can better estimate the path which is submerged by noise, improve the accuracy of parameter estimation, and speed up the iterative convergence speed are shown at the stage of computer simulation.