Erik Lindskog

PhD Thesis, Uppsala University, 314pp, ISBN 91-506-1350-2, May 1999.

The thesis available in Pdf.

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

Outline:

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 multiple antennas.

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 interference.

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.

Abstract:

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.

Contents     [With links to earlier papers related to the chapters.]

1. Space-Time Processing in Wireless Communication     [IEE'97]

2. Channel Estimation        [ICUPC'96]   [EUSIPCO'98]   [VTC'99]   [EUSIPCO'98]

3. Space-Time Decision Feedback Equalization     [ICASSP'95]   [VTC'95]

4. Space-Time ML Sequence Estimation     [VTC'97]

5. Reduced Complexity Space-Time Equalization     [PIMRC'95]   [PIMRC'98]

6. Bootstrap Equalization and Interference Suppression     [Asolomar'95]   [ICUPC'98]

7. Robust Equalization     [ICASSP'93]   [NRS'93]

Related publications:

Licenciate Thesis by Erik Lindskog.
PhD Thesis by Claes Tidestav on multiuser detection.