Numerically Efficient Kalman Filter Based Channel Estimation for OFDM Data Transmission

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 accuracy of the eUD-CF estimator is analyzed by the method of computational experimentation. The simulation results demonstrate that the developed algorithm can effectively restrain the CKFPAs instability problem. The aspects of a parallel implementation of the suggested algorithms are also considered

Recursive Estimation and Identification of Time-Varying Long-Term Fading Channels

This paper is concerned with modeling of time-varying wireless long-term fading channels, parameter estimation, and identification from received signal strength data. Wireless channels are represented by stochastic differential equations, whose parameters and state variables are estimated using the expectation maximization algorithm and Kalman filtering, respectively. The latter are carried out solely from received signal strength data. These algorithms estimate the channel path loss and identify the channel parameters recursively. Numerical results showing the viability of the proposed channel estimation and identification algorithms are presented