Channel Estimation and Prediction from a Bayesian Perspective
Licentiate Thesis, Signals and Systems,
Uppsala University, June 2007
The thesis available in
Paper copies of the thesis can be obtained from
Signals and Systems Group, Uppsala University,
Box 534, SE-75121 Uppsala, Sweden.
Digital communications systems require the receiver to estimate the
bit sequence from a noisy received signal. Estimation is therefore
a crucial part in digital communications. Prediction of error rates, on the
other hand, is not, but it enables capacity improving techniques in the form
of fast link adaptation and opportunistic resource scheduling.
In this thesis, solutions to the estimation and prediction problems are
proposed by inferring radio channels that vary rapidly due to the
users. It is crucial not only to produce point estimates and predictions
channels, but also to take the uncertainty of those estimates into account.
This thesis adopts the Bayesian probability interpretation, which regards
probability theory an extension to logic. Orthodox statistics, which
a probability to be a limiting frequency of an imagined experiment, will in
many cases produce only point estimates, whereas the Bayesian method also
always produces measures of uncertainty.
Linear state space models are designed for a number of system types, and
the Kalman filter is used to infer the time-variant radio channels.
The proposed channel predictor is evaluated on a specifig system proposal.
It is found that control data aiding the channel estimation and prediction
(so called pilot data) should be transmitted simultaneously by all
that the distribution pattern of pilot symbols should also be varied
in order to achieve a high prediction performance.
Two methods for predicting the bit error rate are proposed. It is shown that
although the associated mathematical expressions are somewhat involved,
the numerical complexity induced by those is negligible compared to the
complexity of the channel predictor.
It is also suggested how the proposed algorithms may be used for evaluation
and design of wireless multiuser systems.
OFDMA uplink channel prediction to enable
frequency-adaptive multiuser scheduling.
IEEE PIMRC 2007:
Kalman predictor design for frequency-adaptive
scheduling of FDD OFDMA uplinks.
IST Mobile Summit 2005:
Adaptive TDMA/OFDMA for wide-area coverage and
IEEE ICASSP 2005:
Channel estimation and prediction for adaptive
OFDMA/TDMA uplinks based on overlapping pilots.
Channel estimation and prediction for adaptive OFDM
Wireless IP project complete publication list.
Prediction of mobile radio channels,
Ph.D Thesus by Torbjörn Ekman, 2002.
Wireless IP Project ;
Reseach on channel tracking and prediction
entry in list of publications