基于 k-最近邻和数据生成方法的 Lognormal-Rician 湍流模型参数估计新方法
摘要
本文提出了一种基于 -最近邻 (NN) 和数据生成方法的新颖且高效的参数估计器，用于 Lognormal-Rician 湍流信道。采用Kolmogorov-斯米尔nov (KS) 良好拟合统计工具来探讨 NN 近似在不同信道条件下的有效性，并显示 的选择对近似精度具有重要作用。我们 presented several numerical results to illustrate that solving the constructed objective function can provide a reasonable estimate for the actual values. The accuracy of the proposed estimator is investigated in terms of the mean square error. The simulation results show that increasing the number of generation samples by two orders of magnitude does not lead to a significant improvement in estimation performance when solving the optimization problem by the gradient descent algorithm. However, the estimation performance under the genetic algorithm (GA) approximates to that of the saddlepoint approximation and expectation-maximization estimators. Therefore, combined with the GA, we demonstrate that the proposed estimator achieves the best tradeoff between the computation complexity and the accuracy。
引用
@article{arxiv.2409.01694,
title = {A novel and efficient parameter estimation of the Lognormal-Rician turbulence model based on k-Nearest Neighbor and data generation method},
author = {Maoke Miao and Xinyu Zhang and Bo Liu and Rui Yin and Jiantao Yuan and Feng Gao and Xiao-Yu Chen},
journal= {arXiv preprint arXiv:2409.01694},
year = {2025}
}