The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.

Contributions to Measurement-Based Dynamic MIMO Channel Modeling and Propagation Parameter Estimation

Jussi Salmi

Dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the Faculty of Electronics, Communications and Automation for public examination and debate in Auditorium S1 at Helsinki University of Technology (Espoo, Finland) on the 14th of August, 2009, at 12 noon.

Overview in PDF format (ISBN 978-952-248-019-4)   [4120 KB]
Dissertation is also available in print (ISBN 978-952-248-018-7)


Multiantenna (MIMO) transceivers are a key technology in emerging broadband wireless communication systems since they facilitate achieving the required high data rates and reliability. In order to develop and study the performance of MIMO systems, advanced channel modeling that captures also the spatial characteristics of the radio wave propagation is required. This thesis introduces several contributions in the area of measurement-based modeling of wireless MIMO propagation channels. Measurement based modeling provides realistic characterization of the space, time and frequency dependency of the physical layer for both MIMO transceiver design and network planning.

The focus in this thesis is on modeling and parametric estimation of mobile MIMO radio propagation channels. First, an overview of MIMO channel modeling approaches is given. A hybrid model for characterizing the spatio-temporal structure of measured MIMO channels consisting of a superposition of double-directional, specular-like propagation paths, and a stochastic process describing the diffuse scattering is formulated. State-space modeling approach is introduced in order to capture the dynamic channel properties from mobile channel sounding measurements. Extended Kalman filter (EKF) is employed for the sequential estimation problem and also statistical hypothesis testing for adjusting the model order are introduced. Due to the improved dynamic model of the mobile radio channel, the EKF approach outperforms maximum likelihood (ML) based batch solutions both in terms of lower estimation error as well as computational complexity.

Finally, tensor representation for modeling multidimensional MIMO channels is considered and a novel sequential unfolding SVD (SUSVD) tensor decomposition is introduced. The SUSVD is an orthogonal tensor decomposition having several important applications in signal processing. The advantages of applying the SUSVD instead of other well known tensor models such as parallel factorization and Tucker-models, are illustrated using application examples in channel sounding data processing.

This thesis consists of an overview and of the following 7 publications:

  1. Jussi Salmi, Andreas Richter, Mihai Enescu, Pertti Vainikainen, and Visa Koivunen. 2006. Propagation parameter tracking using variable state dimension Kalman filter. In: Proceedings of the 63rd IEEE Vehicular Technology Conference (VTC 2006-Spring). Melbourne, Australia. 7-10 May 2006, volume 6, pages 2757-2761. © 2006 IEEE. By permission.
  2. Jussi Salmi, Andreas Richter, and Visa Koivunen. 2006. Enhanced tracking of radio propagation path parameters using state-space modeling. In: Proceedings of the 14th European Signal Processing Conference (EUSIPCO 2006). Florence, Italy. 4-8 September 2006. © 2006 by authors.
  3. Jussi Salmi, Andreas Richter, and Visa Koivunen. 2006. State-space modeling and propagation parameter tracking: Multitarget tracking based approach. In: Michael B. Matthews (editor). Conference Record of the 40th Asilomar Conference on Signals, Systems and Computers (ACSSC 2006). Pacific Grove, CA, USA. 29 October - 1 November 2006, pages 941-945. © 2006 IEEE. By permission.
  4. Jussi Salmi, Andreas Richter, and Visa Koivunen. 2007. Tracking of MIMO propagation parameters under spatio-temporal scattering model. In: Michael B. Matthews (editor). Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers (ACSSC 2007). Pacific Grove, CA, USA. 4-7 November 2007, pages 666-670. © 2007 IEEE. By permission.
  5. Jussi Salmi, Andreas Richter, and Visa Koivunen. 2009. Detection and tracking of MIMO propagation path parameters using state-space approach. IEEE Transactions on Signal Processing, volume 57, number 4, pages 1538-1550. © 2009 IEEE. By permission.
  6. Jussi Salmi, Andreas Richter, and Visa Koivunen. 2008. Sequential Unfolding SVD for low rank orthogonal tensor approximation. In: Michael B. Matthews (editor). Conference Record of the 42nd Asilomar Conference on Signals, Systems and Computers (ACSSC 2008). Pacific Grove, CA, USA. 26-29 October 2008, pages 1713-1717. © 2008 IEEE. By permission.
  7. Jussi Salmi, Andreas Richter, and Visa Koivunen. Sequential Unfolding SVD for tensors with applications in array signal processing. IEEE Transactions on Signal Processing, accepted for publication. © 2009 IEEE. By permission.

Keywords: array signal processing, MIMO, radio propagation modeling, sequential estimation, tensors

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© 2009 Helsinki University of Technology

Last update 2011-05-26