Law recognitions by information criteria for the statistical modeling of small scale fading of the radio mobile channel
Information criteria based methods are proposed to select the best probability law to model the distribution of samples resulting from the small-scale fading of the propagation channel. The first is based on the estimation of an optimal histogram approximating the probability density function. The second one employs the direct use of an information criterion. Indeed, the modelling of the radio mobile channel small-scale fading is crucial in digital communications. It is the reason why several propagation models have been implemented to take into account the electromagnetic phenomena inherent in radio wave channels. Amongst these models is the family of statistical distributions which is rapid in computation time. In the context of this study our concern is to find, among different probability laws, the one which best coincides with radio channel behaviour. The experimental results show that the proposed methods are better than those methods already employed, such as the classical KolmogorovSmirnov test using cumulative distribution functions, or methods using different estimators of probability density functions, like the kernel density estimator and the Gaussian mixture model. Results are provided in supervised and unsupervised contexts.
information criteria model selection probability density function approximation (histogram kernel method gaussian mixture) kolmogorovsmirnov distance radio mobile propagation channel