Gradient Algorithms for ML Estimation of Time-Varying FIR Channels

This paper considers maximum likelihood (ML) estimation of time-varying finite impulse response (FIR) channels. The channel is modelled as a tapped-delay line filter with complex coefficients. The complexity of the likelihood function and the large number of parameters to be estimated, make analytical maximization of the likelihood function infeasible. Taking the structure of the channel tap covariance matrix into account, we consider gradient algorithms to perform constrained ML estimation of the channel parameters.