Adaptive Channel Prediction Based on Polynomial Phase Signals

Paper i proceeding, 2008

Motivated by recently published physics based scattering SISO and MIMO channel models, a new adaptive channel prediction using Kalman filter based on non-stationary polynomial phase signals with time-varying amplitudes is proposed. To mitigate the influence of the time-varying amplitudes on parameter estimation, an iterative estimation using the Non-linear instantaneous LS criterion is proposed, where the number of signal components and model orders are known. The new predictor outperforms the classical Linear Prediction and stationary sinusoidal modeling based prediction in Monte Carlo simulations.