Long Range Channel Prediction Based on Non-Stationary Parametric Modeling
Artikel i vetenskaplig tidskrift, 2009
Motivated by the analysis of measured radio channels and recently published physics-based scattering SISO and MIMO channel models, a new approach of long-range channel prediction based on nonstationary multicomponent polynomial phase signals (MC-PPS) is proposed. An iterative and recursive method for detecting the number of signals and the orders of the polynomial phases is proposed. The performance of these detectors and estimators is evaluated by Monte Carlo simulations. The performance of the new channel predictors is evaluated using both synthetic signals and examples of real world channels measured in urban and suburban areas. High-order polynomial phase parameters are detected in most of the measured data sets, and the new methods outperform the classical LP in given examples for long-range prediction for the cases where the estimated model parameters are stable. For the more difficult data sets, the performance of these methods are similar, which provides alternatives for system design when other issues are concerned.
Adaptive Kalman filtering