Long Range Channel Prediction Based on Non-Stationary Parametric Modeling
Journal article, 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.

Rayleigh channels

Adaptive Kalman filtering

Prediction methods

Radio propagation

Nonlinear estimation

Author

Ming Chen

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Mats Viberg

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 57 2 622-634

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

More information

Created

10/8/2017