Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this Paper, an estimator of the α-κ-μ distribution, based on the maximum likelihood (ML) estimation method, is proposed and verified. The technique is applied in samples generated by the Smith spectrum method, which is also a contribution in this paper. This distribution has been recently proposed to model the received field strength in a nonlinear environment with line-of-sight condition. Hence, efficient estimators are required to infer the parameters of the received signal amplitude distribution in propagation mediums modeled by it. The ML estimator for the α-κ-μ distribution is derived and verified against chosen true values of the parameters by means of a simulation.  verify that, for a large sample size, the estimators are unbiased, from a practical point of view, for the useful range of α, κ and μ. The results conclude that the estimator variations about its sample mean are smaller for the α parameter.
['Fernando Palma Batista', 'Rausley Adriano Amaral de Souza', 'Antonio Marcelo Oliveira Ribeiro']
I am writing a research paper on a idea Maximum likelihood estimator for the α-κ-μ fading environment
[{"from": "human", "value": "I am writing a research paper on a idea Maximum likelihood estimator for the \u03b1-\u03ba-\u03bc fading environment"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this Paper, an estimator of the \u03b1-\u03ba-\u03bc distribution, based on the maximum likelihood (ML) estimation method, is proposed and verified. The technique is applied in samples generated by the Smith spectrum method, which is also a contribution in this paper. This distribution has been recently proposed to model the received field strength in a nonlinear environment with line-of-sight condition. Hence, efficient estimators are required to infer the parameters of the received signal amplitude distribution in propagation mediums modeled by it. The ML estimator for the \u03b1-\u03ba-\u03bc distribution is derived and verified against chosen true values of the parameters by means of a simulation.  verify that, for a large sample size, the estimators are unbiased, from a practical point of view, for the useful range of \u03b1, \u03ba and \u03bc. The results conclude that the estimator variations about its sample mean are smaller for the \u03b1 parameter."}]