Distance measures for signal processing and pattern recognition
Signal Processing
Wireless Communications
Computer Vision and Image Understanding
Law recognition via histogram-based estimation
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Entropy minimization for supervised digital communications channelequalization
IEEE Transactions on Signal Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
Density estimation by stochastic complexity
IEEE Transactions on Information Theory - Part 2
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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 Kolmogorov-Smirnov 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.