In this paper, the probability density function (PDF) estimation is introduced in the framework of estimating the\r\nNakagami fading parameter. This approach provides an analytic procedure for finding the fading parameter. Using the\r\ncopula theory, an accurate PDF estimate is obtained even when the desired signal is corrupted in a noisy\r\nenvironment. In the real world, the noise samples could be highly dependent on the main signal. Copula-based\r\nmodels are a general set of statistical models defined for any multivariate random variable. Thus, they depict the\r\nstatistical behavior of a received signal including two dependent terms, representative of the desired signal and noise.\r\nPrevious works in the Nakagami parameter determination have mainly examined estimation based on either a\r\nnoiseless sample model or an independent trivial noisy one. In this paper, we consider a more comprehensive\r\nsituation about the noise destruction and our investigation is done in low signal-to-noise ratios. The parametric\r\nbootstrap method approves the accuracy of the analytically estimated PDF, and simulation results show that the new\r\nestimator has superior performance over conventional estimators.
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