基于经验分布函数快速收敛的信噪比估计器

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.