Prediction of Mobile Radio Channels
Modeling and Design
PhD Thesis, Uppsala University,
Oct. 2002, 254 pp.
The thesis available in Postscript :
Paper copies of the thesis can be obtained from
Signals and Systems Group, Uppsala University,
Box 534, SE-75121 Uppsala, Sweden.
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One of the main problems in mobile radio communication
is the rapid variation (fading) of the signal power at the
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.
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.
Mobile radio channel, fading channel model, channel estimation,
channel prediction, power prediction.
by Torbjörn Ekman,
which also discusses other nonlinear estimators
and adaptive estimators.
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).
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.