A NEW APPROACH TO WIRELESS CHANNEL MODELING USING FINITE MIXTURE MODELS

This paper presents a new approach to modeling a wireless channel using finite mixture models (FMM). Instead of the conventional approach of using non mixtures (single) probability distribution functions, FMMs are used here to model the channel impulse response amplitude statistics. To demonstrate this, a FMM based model of Ultrawideband (UWB) channels amplitude statistics is developed. In this research, finite mixture models composed of combinations of constituent PDFs such as Rayleigh, Lognormal, Weibull, Rice and Nakagami are used for modeling the channel amplitude statistics. The use of FMMs is relevant because of their ability to characterize the multimodality in the data. The stochastic expectation maximization (SEM) technique is used to estimate the parameters of the FMMs. The resultant FMMs are then compared to one another and to non-mixture models using model selection techniques such as Akaike’s Information Criteria (AIC). Results indicate that models composed of a mixture of Rayleigh and Lognormal distributions consistently provide good fits for most of the impulses of the UWB channel. Other model selection techniques such as Minimum Description Length (MDL) and Accumulative Predictive Error (APE) also confirmed this finding. This selection of FMM based on Rayleigh and Lognormal distributions is true for both the industrial as well as the university environment channel data

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