Wiener filter-based channel predictor performance improvement using polinomial extrapolation

The widely used channel predictor today is Wiener filter-based channel predictor, where the channel is assumed to be random and has time invariant correlation value. Based on the quasi static principle, in the interval of short observation time the channel tends to be deterministic and has time variant correlation value. This makes Wiener filter-based channel predictor can not yield minimum mean squared error. To solve this problem the filter should accommodate the deterministic property and the correlation fluctuation of the channel. This can be achieved by polinomially extrapolating the channel correlation value. This work conducted performance evaluation of conventional Wiener filter and Wiener filter with polinomial extrapolation modification. The performance was evaluated by varying the symbol rates, Doppler frequency and the filter orders. However, the performance of polinomial extrapolation method for the noise was also observed.