AN ADVANCED METHOD FOR CHANNEL FADING PARAMETER ESTIMATION BASED ON THE GENERALIZED GAMMA DISTRIBUTION

Abstract

In this manuscript, we present statistical characterization of the fading of the radio channels is of crucial importance to the planning and testing of today's mobile communication networks. The extended gamma (Stacy) variation is a very flexible fading model and includes a number of common distributions, e.g. Rayleigh, Gamma, Weibull, and Nakagami-m. Although it is a general model, it has not been easy to obtain reliable estimates of its key parameters, especially in cases where limited measurement data are available. In this experiment, a novel Psi-inverse (PI) parameter estimation method for the generalized gamma fading model is suggested and its performance is evaluated with respect to a maximum likelihood estimator. The proposed method is based on the use of digamma-based transformations to achieve better numerical stability and numerical accuracy. Its performance is systematically compared to conventional estimation methods, e.g. method-of-moments and skewness-logarithmic estimator. Extensive Monte Carlo simulations are carried out in many different fading scenarios and sample sizes typical of real-world wireless scenarios. The results demonstrate the PI estimator is always superior to the existing methods especially in the regimes of small or moderate samples, where the conventional ones tend to be biased and unstable. While the maximum likelihood estimator is fine for large data sets, the estimator is not as reliable if the availability of the data is limited. The major achievement of this work is the introduction of a powerful parameter estimation method with computational efficiency, which yields a great increase in the estimation accuracy under actual operational conditions. The proposed method is well suited for practical applications for wireless channel modeling, system simulation and analysis of performance of communication systems operated in complex fading environment.

Keywords : Fading radio signals,  distribution, the Stacy distribution, Gamma distribution, Erlang distribution, Chi-squared distribution, Nakagami distribution, size biased distributions, ML estimators.