Torbjörn Ekman

PhD Thesis, Uppsala University, ISBN 91-506-1625-0 Oct. 2002, 254 pp.

The thesis available in Pdf: 4689KB.

Paper copies of the thesis can be obtained from Ylva Johansson, Signals and Systems Group, Uppsala University, Box 534, SE-75121 Uppsala, Sweden.

Outline:

One of the main problems in mobile radio communication is the rapid variation (fading) of the signal power at the receiver. New adaptive broadband packet data communication systems will use feedback of the channel conditions. In such systems, the presence of fading can be turned into an advantage. If the receiving conditions of the users are known in advance, the channel resource can be allocated to the users who can use it most effectively (multiuser diversity). Furthermore, coding, modulation and/or transmit power can be adjusted to the current receiving conditions (fast link adaptation).

Both of these schemes require that the receiving conditions of the users are known in advance at the transmitter. Predictors of the channel states will thus constitute a crucial components of adaptive broadband transmission systems to mobile users. This thesis is devoted to the problem of predicting the mobile radio channel.

Abstract:

Prediction of the rapidly fading envelope of a mobile radio channel enables a number of capacity improving techniques like fast resource allocation and fast link adaptation. This thesis deals with linear prediction of the complex impulse response of a channel and unbiased quadratic prediction of the power. The design and performance of these predictors depend heavily on the correlation properties of the channel. Models for a channel where the multipath is caused by clusters of scatterers are studied. The correlation for the contribution from a cluster can be approximated as a damped complex sinusoid. A suitable model for the dynamics of the channel is an ARMA-process. This motivates the use of linear predictors.

A limiting factor in the prediction is the estimation errors on the observed channels. This estimation error, caused by measurement noise and time variations, is analyzed for a block based least squares algorithm which operates on a Jakes channel model. Efficient noise reduction on the estimated channel impulse responses can be obtained with Wiener-smoothers that are based on simple models for the dynamics of the channel combined with estimates of the variance of the estimation error.

Power prediction that is based on the squared magnitude of the linear prediction of the taps will be biased. Hence, a bias compensated power predictor is proposed and the optimal prediction coefficients are derived for the Rayleigh fading channel. The corresponding probability density functions for the predicted power are also derived. A performance evaluation of the prediction algorithm is carried out on measured broadband mobile radio channels. The performance is highly dependent on the variance of the estimation error and the dynamics of the individual taps.

Keywords:

Mobile radio channel, fading channel model, channel estimation, channel prediction, power prediction.

Related publications:

PhD Thesis by Rikke Apelfröjd, 2018 on channel estimation and prediction for 5G applications.

Technical Report by Rikke Apelfröjd 2018 on Kalman prediction of multipoint downlinks.

Licenciate Thesis by Torbjörn Ekman, which also discusses other nonlinear estimators and adaptive estimators.

VTC02 paper on the improved unbiased power predictor, evaluated on 39 measured channels. (Contains early version of the results of Chapter 7 in the PdD thesis).

IEEE TCOM paper on using the predictor error variance for optimizing adaptive modulation (Chapter 8 of the Thesis)

VTC01s paper on linear prediction performance on 45 measured channels.

VTC01s paper on the analysis of the LS Estimation error on a Rayleigh fading channel.

VTC 1999 paper on quadratic and linear subsampled filters for prediction.