Parameter Estimation in Wireless Channel Networks using Second Order Statistics

A stochastic differential equation of a general form is considered for modeling wireless channels and the model parameters are estimated using second order statistics. More exactly, the parameters are estimated by minimizing a loss function that consists of squared differences between estimated and theoretical covariance elements, where the latter elements are parameterized by the unknown parameters. An asymptotic expression for the covariance matrix of the estimated parameter vector is given. The variances given by this expression are compared with empirical variances from a Monte Carlo simulation and with the Cramer-Rao lower bound.