一种基于高阶统计信息的信噪比估计改进算法

Improved SNR Estimation Algorithm of Higher-Order Statistics

针对多进制正交幅度调制(Multiple quadrature amplitude modulation,MQAM)信号在低信噪比条件下估计精度不高的问题,提出了一种基于综合利用高阶统计信息的信噪比估计改进算法。根据所选高阶统计量最高阶数的不同,建立了3种信噪比与多种高阶统计量运算式之间的线性关系,利用全回归线性分析方法将3种线性关系转化为3种全回归模型,并求解模型系数。该算法充分利用了多种高阶统计量的有用信息,提高了信噪比估计精度。MQAM的仿真结果表明:在低信噪比条件下,该算法减小了信噪比估计误差,其估计性能明显优于传统的其他算法,且3种模型估计性能依次增加,可依据不同的信噪比要求对3种模型进行选取。

To solve the low accuracy of multiple quadrature amplitude modulation（MQAM） signal-to-noise（SNR） estimation under the low SNR condition,an improved SNR estimation algorithm based on comprehensive utilization of higher-order statistics is proposed.According to the difference between the topmost rank of the higher-order statistics,the linearity relationships of three kinds of SNR and various higher-order statistics expression are created.And they are converted into three kinds of whole regression patterns with a whole regression linear analysis method.Then the pattern coefficients are solved.The proposed algorithm can fully use the useful information of the higher-order statistics,and increase the accuracy of SNR estimation.Simulation results show that under the low SNR condition,the algorithm can reduce SNR estimating errors and perform better than other traditional algorithms.Moreover,the estimation performance of the three kinds of patterns gradually increases in turn,and a proper pattern can be selected according to the different SNR request.