Abstract
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 nested Markov chain, where the macrostate variation is assumed to be caused by the shadowing process while microstate transitions is initialized by fading effect with macrostate-dependent transition probabilities. As an illustration of the mathematical formalism, some simulation results are provided and discussed.