基于经验分布函数快速收敛的信噪比估计器

通过Kolmogorov-Smirnov检验,基于经验分布函数（EDF）的信噪比估计器在宽信噪比范围内对各种多级星座的信噪比估计都是有效的.然而,在本地累积分布函数（CDF）和EDF之间需要进行大量的匹配操作和加法运算.基于这个问题,提出了一种通过线性多项式连续迭代来加速匹配过程的信噪比估计器.在保证估计精度的前提下,使用"以直代曲"的思想,用线性多项式的根不断迭代逼近最大距离曲线的零点,并将零点所对应的信噪比作为接收信号信噪比的估计值.仿真结果表明,与原算法估计器相比,该方法的迭代次数减少了90%以上,降低了原算法的匹配复杂度和运算量.与现有降复杂度的估计器相比,该估计器具有更快的收敛速度和更好的估计性能. 

Signal-to-Noise Ratio Estimator with Fast Convergence Based on Empirical Distribution Function

Empirical distribution function (EDF)-based estimators are effective for various multilevel constellations in a wide signal-to-noise ratio (SNR) range via the Kolmogorov-Smirnov test. However,there are numerous addition and matching operations between reference cumulative distribution functions (CDFs) and the EDF. A signal-to-noise ratio estimator through continuous iteration with a linear polynomial to accelerate the matching procedure was proposed. On the premise of estimation accuracy,using the idea of "direct substitution curve",the zero point of the maximum distance curve was iteratively approximated by the root of the linear polynomial,and the SNR corresponding to the zero point was used as the estimation value of the received signal. The simulation results show that compared with the original algorithm,the iteration number of the proposed strategy is reduced by more than 90%,which greatly reduces the matching complexity and computational complexity. Compared with the existing reduced-complexity iterative strategy,the proposed strategy exhibited faster convergence and better estimation performance.