.png){kind=icon}
返回
A differential evolution algorithm for estimating mobile channel parameters α - η - μ
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.114357.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The statistical modeling of mobile radio signals requires the estimation of parameters that describe the probability distribution that hypothetically models this channel, so that this probabilistic model guarantees a good adjustment to the experimental data. This article proposes the use of differential evolution (DE) algorithms for estimating parameters of the alpha - eta - mu. fading channel, and to compare these to the traditional method of moments (MM) and maximum likelihood estimation (MLE) method. These traditional parameter estimation methods use nonlinear numerical methods, and the solution, if found, may be the optimal value, an approximation of the optimal value, or a local maximum. The authors demonstrate through comparative experiments using the MM and the MLE method that the DE algorithm for the proposed estimation demands a lower run time. In addition, it presents the error performance measured by the mean square error (MSE), near or above, as well as high robustness measured by the statistical analysis. Essentially, this algorithm always finds acceptable physical estimations with a good goodness of fit to experimental data. This estimating DE algorithm along with its proposed fitness function are original contributions of this paper. The received signal samples, used in the experiments of this paper, were randomly generated by the alpha - eta - mu. fading simulator, which is another contribution of this paper. This proposed alpha - eta - mu. fading simulator is based on the Clarke and Gans fading model and expands the generation range of current simulators, from mu integer multiples of 0.5, to mu integer multiples of 0.25.
Keyword:
alpha - eta - mu fading channel
Differential evolution
Maximum likelihood estimation
Numerical optimization
Parameter estimation
Random generators
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
分享收藏Synthesis and reactions of tris(tetramethylethylenediaminelithium)hexamethylmetallates of rhodium, iridium and ruthenium(III) and of hexakis(neopentyl)(μ-oxo)dirhodium(IV). X-ray crystal structures of fac-RhMe3(CO)(Me2NCH2CH2NMe2), fac-RhMe3(C4H8S)(Me2NCH2CH2NMe2), fac-RhMe3(PMe3)3 and [(Me3CCH2)3Rh]2O.png)
Polyhedron
PolyhedronIF0
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏分享收藏分享收藏
查看更多内容