Channel Estimation and Prediction for MIMO OFDM Systems:
Key design and performance aspects of Kalman-based algorithms.
PhD Thesis, Uppsala University,
February 2011, 245 pp.
Dissertation in Engineering Science with specialization
in Signal Processing, publicly examined
in Polhemssalen, Ångström Laboratory,
Uppsala on Friday March 25, 2011 at 13.15.
Thesis Opponent: Prof. Ove Edfors, Lunds universitet, Lund, Sweden.
The thesis available
Abstract in DIVA database
Paper copies of the thesis
can be obtained from Ylva Johansson,
Signals and Systems Group, Uppsala University,
Box 534, SE-75121 Uppsala, Sweden.
Wireless broadband systems based on Orthogonal Frequency Division
Multiplexing (OFDM) are being introduced to meet demands for high data
In multiple users systems, the available bandwidth has to be shared
efficiently by several users. The radio channel quality will fluctuate,
or fade, as users move.
Fading complicates the resource allocation, but channel prediction may
alleviate this problem. A flexible and computationally inexpensive state
space representation of fading channels is here used in conjunction with
a Kalman filter, operating on special-purpose reference signals, to
track and predict fading OFDM channels.
The thesis investigates key design and performance aspects of such
Taking a probabilistic approach, we interpret the output of the Kalman
filter as a full representation of a state of knowledge about the fading
channels, given whatever information is at hand.
For systems analysis,
this permits conclusions to be drawn about channel estimation and
prediction performance based on only vague information about the fading
characteristics of the channel rather than on actual channel
measurements. This is an alternative to conducting classic simulation
Various reference signal designs are studied and good design
choices are recommended. Superimposed reference signal schemes are also
proposed for and evaluated in cases where multiple signals are received,
e.g. in multi-user (MU), multi-input multi-output (MIMO), or coordinated
multi-point (CoMP) settings. By using time-varying reference signals,
channel estimation and prediction performance is shown to be improved
considerably in crowded frequency bands.
The variation of prediction
performance with prediction range and Doppler spectrum characteristics
For link adaptation, we derive the appropriate metric
on which adaptation decisions should be based. The probability density
function for this metric is derived for general MIMO channels. Link
adaptation is studied for a single link system when channel prediction
and estimation errors are present, both for uncoded systems and systems
using large block codes with soft decoders.
Various aspects of channel model acquisition are addressed by conducting
studies on measured channels. Owing to the use of special matrix
structures and fast convergence to time-invariant or periodic solutions,
we find the Kalman filter complexity to be reasonable for future
Finally, expressions for the impact of modelling errors
are derived and used to study the impact of modelling errors on channel
prediction performance in some example cases.
Channel estimation, channel prediction, OFDM, Kalman filtering.
Table of Contents:
- Wireless communications
- Linear filtering and inference theory
- Modelling MIMO-OFDMA systems
- A channel estimation case study
- The OFDMA uplink design
- Link adaptation for uncertain channel state information
- Studies on measured channels
- Modelling errors
References on prediction of mobile radio channels:
Proceedings of the IEEE paper (2007, Invited Paper),
giving an overview of adaptive transmission in OFDMA systems,
also using channel prediction.
by Daniel Aronsson, 2007.
IEEE PIMRC 2007 paper on
Kalman predictor design for frequency-adaptive scheduling
of FDD OFDMA uplinks.
EUSIPCO 2007 paper
on OFDMA uplink channel prediction.
IEEE ICASSP 2005 paper
on channel estimation and prediction for adaptive
OFDMA/TDMA uplinks based on overlapping pilots.
on adaptive TDMA/OFDMA for wide-area coverage and
VTC 2003-paper on
Channel estimation and prediction for adaptive OFDM downlinks.
by Torbjörn Ekman, 2002.
Conference Paper on unbiased power prediction of
broadband radio channels.
Conference Paper on long-term prediction of
broadband radio channels.
by Torbjörn Ekman, summarizing our results up to november 2000.
Conference Paper on
linear and quadratic predictors.
Our research on channel prediction
Wireless IP and WINNER projects
Entry in publ. list
This material is presented to ensure timely dissemination
of scholarly and technical work. Copyright and all rights
therein are retained by authors.
All persons copying this information are expected to
adhere to the terms and constraints invoked by each authors
copyright. This work may not be reposted
without the explicit permission of the copyright holders.