Parameter Estimation for Stochastic Channel Models using Temporal Moments

This paper proposes a method to infer on the parameters of a stochastic channel model from observations of temporal moments without multipath extraction. The distribution of the temporal moments is approximated to be Gaussian, and sampling is carried out from the approximate posterior. The temporal moments are found to be informative about the model parameters, as the parameters can be recovered from the samples.