Radio Channel Prediction Based on Parametric Modeling
Long range channel prediction is a crucial technology for future wireless communications. The prediction of Rayleigh fading channels is studied in the frame of parametric modeling in this thesis.
Suggested by the Jakes model for Rayleigh fading channels,
deterministic sinusoidal models were adopted for long range
channel prediction in early works. In this thesis, a number of new channel predictors based on stochastic sinusoidal modeling are proposed. They are termed conditional and unconditional LMMSE predictors respectively. Given frequency estimates, the amplitudes
of the sinusoids are modeled as Gaussian random variables in the conditional LMMSE predictors, and both the amplitudes and frequency estimates are modeled as Gaussian random variables in the unconditional LMMSE predictors. It was observed that a part of the channels cannot be described by the periodic sinusoidal bases, both in simulations and measured channels. To pick up this
un-modeled residual signal, an adjusted conditional LMMSE
predictor and a Joint LS predictor are proposed.
Motivated by the analysis of measured channels and recently
published physics based scattering SISO and MIMO channel models, a new approach for channel prediction based on non-stationary Multi-Component Polynomial Phase Signal (MC-PPS) is further proposed. The so-called LS MC-PPS predictor models the amplitudes of the PPS components as constants. In the case of MC-PPS with time-varying amplitudes, an adaptive channel predictor using the Kalman filter is suggested, where the time-varying amplitudes are
modeled as auto-regressive processes. An iterative detection and estimation method of the number of PPS components and the orders of polynomial phases is also proposed. The parameter estimation is based on the Nonlinear LS (NLLS) and the Nonlinear Instantaneous
LS (NILS) criteria, corresponding to the cases of constant and time-varying amplitudes, respectively.
The performance of the proposed channel predictors is evaluated using both synthetic signals and measured channels. High order polynomial phase parameters are observed in both urban and suburban environments. It is observed that the channel predictors based on the non-stationary MC-PPS models outperform the other predictors in Monte Carlo simulations and examples of measured
urban and suburban channels.
polynomial phase signal
EA, Hörsalsvägen 11, Göteborg, Sweden
Opponent: Professor Christoph Mecklenbräuker, Institute of Communications and Radio-Frequency Engineering, Vienna University of Technology, Austria