A novel and efficient parameter estimation of the Lognormal-Rician turbulence model based on k-Nearest Neighbor and data generation method
<|reference_start|>A novel and efficient parameter estimation of the Lognormal-Rician turbulence model based on k-Nearest Neighbor and data generation method: 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.<|reference_end|>
@article{miao2024a,
    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, Xinyu Zhang, Bo Liu, Rui Yin, Jiantao Yuan, Feng Gao,
  Xiao-Yu Chen},
    journal={arXiv preprint arXiv:2409.01694},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01694},
    primaryClass={eess.SP cs.NA math.NA}
}