Adaptive Modeling, Prediction, and Tracking of Wireless Fading Channels

A key element for many fading-compensation techniques is long-range prediction of the fading channel. A linear approach, usually used to model the time evolution of the fading process, does not perform well for long-range prediction. In this article, we propose an adaptive channel prediction algorithm using a state-space approach for the fading process based on the sum-sinusoidal model. Our simulations show that this algorithm significantly outperforms the conventional linear method, for both stationary and non-stationary fading processes, especially for long-range predictions. The self-recovering structure, as well as the reasonable and steady computational complexity, makes the proposed algorithm appealing for practical applications1.