A hierarchical Markov model for wireless shadowed fading channels

A novel model for slowly varying shadowed fading channel based on Markov chain theory is proposed. It not only leads to accurate first-order statistical description of the composite process but also captures the memory effect of the channel. Following a hierarchical approach, the shadowing/fading process is modeled as a nested Markov chain, where the macrostate variation is assumed to be caused by the shadowing process while microstate transitions is initialized by the fading effect with macrostate-dependent transition probabilities. As an illustration of the mathematical formalism, some simulation results are provided and discussed.