Space-time Processing and Equalization
for Wireless Communications
PhD Thesis, Uppsala University,
314pp, ISBN 91-506-1350-2, May 1999.
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.
With multiple antennas, received and transmitted signals can be
separated not only with temporal processing but also with spatial
processing. We call the combination of spatial and temporal processing
space-time processing. Space-time processing is a
tool for improving the overall economy and efficiency
of a digital cellular radio system by exploiting the use of
Most current cellular radio modems do not, however,
efficiently exploit the
spatial dimension offered by multiple antennas.
The spatial domain can be used to reduce co-channel
interference, increase diversity gain, improve array gain,
and reduce intersymbol
These improvements can have
significant impact on the overall performance of a wireless network.
The aim of this thesis is to develop, explore and investigate signal
processing algorithms that combine spatial and
temporal processing, to attain
results which cannot be obtained by either
spatial or temporal processing individually.
In this thesis several aspects of space-time processing and
equalization for wireless communications are treated.
We discuss several different methods of improving
estimates of space-time channels, such as temporal
parametrization, spatial parametrization, reduced rank
channel estimation, bootstrap channel estimation,
and joint estimation of an FIR channel and an AR noise model.
In wireless communication the signal is often
subject to intersymbol interference
as well as interference from other users.
We here discuss space-time decision feedback equalizers
and space-time maximum likelihood sequence estimators,
which can alleviate the impact of these factors.
In case the wireless channel does not experience a
large amount of coupled delay and angle spread,
sufficient performance may be obtained by an equalizer
with a less complex structure. We therefore discuss
various reduced complexity equalizers and
symbol sequence estimators.
We also discuss re-estimating the channel and/or re-tuning
the equalizer with a bootstrap method using estimated symbols.
With this method we can improve the performance of the
channel estimation, the equalization, and the interferer
suppression. This method can also be used to
suppress asynchronous interferers.
When equalizers and symbol detection algorithms
are designed based on estimated channels we need to
consider how errors in the estimated channels, or errors
due to time variations, affect the performance of the equalizer
or symbol detector. We show that equalizers tuned
based on ordinary least squares estimated channels
exhibit a degree of self-robustification, which automatically
compensates for potential errors in the channel estimates.
[With links to earlier papers related to the chapters.]
- 1. Space-Time Processing in Wireless Communication
- 2. Channel Estimation
- 3. Space-Time Decision Feedback Equalization
- 4. Space-Time ML Sequence Estimation
- 5. Reduced Complexity Space-Time Equalization
- 6. Bootstrap Equalization and Interference Suppression
- 7. Robust Equalization
- Related publications:
Licenciate Thesis by Erik Lindskog.