Uppsala universitet
Channel Estimation and Prediction for MIMO OFDM Systems:
Key design and performance aspects of Kalman-based algorithms.

Daniel Aronsson

PhD Thesis, Uppsala University, ISBN 978-91-506-2194-5, February 2011, 245 pp.

Dissertation in Electrical Engineering 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 in Pdf.

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 transfer rates. 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 estimators.

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 studies.

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 is investigated.

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 implementation.

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:
  1. Introduction
  2. Wireless communications
  3. Linear filtering and inference theory
  4. Modelling MIMO-OFDMA systems
  5. A channel estimation case study
  6. The OFDMA uplink design
  7. Link adaptation for uncertain channel state information
  8. Studies on measured channels
  9. Modelling errors
References on prediction of mobile radio channels:
PhD Thesis by Rikke Apelfröjd, May 2014 on channel estimation and prediction for 5G applications.
Technical Report by Rikke Apelfröjd 2018 on Kalman prediction of multipoint downlinks.
Proceedings of the IEEE paper (2007, Invited Paper), giving an overview of adaptive transmission in OFDMA systems, also using channel prediction.
Licenciate Thesis 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.
IST-Summit-2005 paper on adaptive TDMA/OFDMA for wide-area coverage and vehicular velocities.
VTC 2003-paper on Channel estimation and prediction for adaptive OFDM downlinks.

PhD Thesis by Torbjörn Ekman, 2002.
VTC 2002-Fall Conference Paper on unbiased power prediction of broadband radio channels.
VTC 2001-Spring Conference Paper on long-term prediction of broadband radio channels.
Licenciate Thesis by Torbjörn Ekman, summarizing our results up to november 2000.
VTC 1999Fall Conference Paper on linear and quadratic predictors.

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