A novel and efficient parameter estimation of the Lognormal-Rician turbulence model based on k-Nearest Neighbor and data generation method

TL;DR

This paper introduces a new parameter estimation method for the Lognormal-Rician turbulence model using k-Nearest Neighbor and data generation, demonstrating improved accuracy and computational efficiency through genetic algorithms.

Contribution

It presents a novel estimator combining kNN, data generation, and genetic algorithms for the Lognormal-Rician model, balancing accuracy and complexity.

Findings

The estimator's accuracy is validated with numerical results.

Increasing sample size beyond a point does not significantly improve performance.

Genetic algorithm-based estimation outperforms gradient descent in accuracy.

Abstract

In this paper, we propose a novel and efficient parameter estimator based on $k$-Nearest Neighbor ($k$NN) and data generation method for the Lognormal-Rician turbulence channel. The Kolmogorov-Smirnov (KS) goodness-of-fit statistical tools are employed to investigate the validity of $k$NN approximation under different channel conditions and it is shown that the choice of $k$ plays a significant role in the approximation accuracy. We present 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.