Adaptive prediction, tracking and power adjustment for frequency non-selective fast fading channels

Previously, the authors introduced a novel algorithm for long range prediction of fading channels. This algorithm finds the linear minimum mean squared error (MMSE) estimate of the future fading coefficients given a fixed number of previous observations. In this paper, we show that the superior performance of this algorithm is due to its lower sampling rate relative to the conventional (data rate) methods of fading prediction. We also enhance the algorithm by an adaptive prediction and tracking method that increases accuracy and maintains the robustness of long-term prediction as the physical channel parameters vary. Finally, we show that large improvements in the bit error rate (BER) are possible when the proposed prediction method is used. In particular, analysis of the channel inversion with threshold technique is presented to demonstrate that the BER can be reduced to the level of and beyond the additive white Gaussian noise (AWGN) channel