Dynamic power estimation and prediction in composite fading-shadowing channels

We present methods for dynamic estimation and prediction of local mean (shadow) powers from instantaneous signal powers in composite fading-shadowing wireless communication scenarios. We adopt a Nakagami-m fading model for the instantaneous signal powers and a first-order autoregressive - AR(1) - model for the shadow process in decibels. Sequential Bayesian analysis is applied to estimate the shadow powers assuming that the Nakagami-m and AR(1) model parameters are known. We also develop a method for jointly estimating both the shadow powers and unknown model parameters. Numerical simulations demonstrate the performance of the proposed methods.