Numerically efficient UD filter based channel estimation for OFDM wireless communication technology

Channel estimation and prediction algorithms are developed for use in broadband OFDM data transmission over non-ideal channels. The scalar complex channel coefficients are described by Gauss-Markov AR models of a given order in state space form to model the channel fading statistics. On this basis, the conventional Kalman filtering and prediction algorithm (CKFPA) is presented as a starting point for further development. A novel numerically stable channel estimation algorithm based on the original KFPA solution, the so-called extended Array UD Covariance Filter (eUD-CF) algorithm, is developed. The aspects of a parallel implementation of the suggested algorithm are also considered.