A novel adaptive mapping from physical measurements in a non-stationary wireless environment to a variable length Markov chain (VLMC) model is proposed in this research. The proposed scheme consists of two main components: the estimation of channel signal-to-noise ratio (SNR) distribution and discrete VLMC modeling. To obtain the channel SNR distribution, a kernel density estimation algorithm is used to track local changes of channel statistics resulting from varying mobile environments. With the estimated channel SNR distribution an iterative partitioning mechanism is performed to construct the VLMC model, which yields a much larger and structurally richer class of models than ordinary higher order Markov chains. Application of this model is presented, which is the computation of fading parameters such as the fading duration and the level crossing rate. The accuracy of the proposed VLMC scheme and the performance of its applications are demonstrated via simulation in a micro-cell non-stationary wireless environment.
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