Complexity reduction for vehicular channel estimation using the filter-divergence measure
(2011) Asilomar Conference on Signals, Systems, and Computers, 2010- Abstract
- In vehicular communications systems the channel estimation filter suppresses the additive noise in channel estimates obtained from pilot symbols. Vehicular channels show local stationarity for a finite region in time and frequency, only. Such a process can be divided into consecutive stationarity regions, allowing to calculate a Wiener filter. We analyze the increase of the mean square error (MSE) when using a mismatched Wiener filter calculated for a past stationarity region. The MSE increase of the filter is related to a new metric, the filter divergence directly observable at the receiver side. By accepting an increase of MSE, measured by the filter-divergence, the same filter coefficients can be used for several stationarity regions,... (More)
- In vehicular communications systems the channel estimation filter suppresses the additive noise in channel estimates obtained from pilot symbols. Vehicular channels show local stationarity for a finite region in time and frequency, only. Such a process can be divided into consecutive stationarity regions, allowing to calculate a Wiener filter. We analyze the increase of the mean square error (MSE) when using a mismatched Wiener filter calculated for a past stationarity region. The MSE increase of the filter is related to a new metric, the filter divergence directly observable at the receiver side. By accepting an increase of MSE, measured by the filter-divergence, the same filter coefficients can be used for several stationarity regions, allowing computational complexity reduction in a real system. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1661168
- author
- Bernadó, Laura ; Zemen, Thomas ; Paier, Alexander and Kåredal, Johan LU
- organization
- publishing date
- 2011
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- Asilomar Conference on Signals, Systems, and Computers, 2010
- conference location
- Pacific Grove, CA, United States
- conference dates
- 2010-11-07 - 2010-11-10
- external identifiers
-
- scopus:79957979084
- ISBN
- 978-1-4244-9721-8
- 978-1-4244-9722-5
- DOI
- 10.1109/ACSSC.2010.5757485
- language
- English
- LU publication?
- yes
- id
- 7efeddac-3e17-4722-9491-07a178fb9acc (old id 1661168)
- date added to LUP
- 2016-04-04 13:39:25
- date last changed
- 2024-01-20 05:09:48
@inproceedings{7efeddac-3e17-4722-9491-07a178fb9acc, abstract = {{In vehicular communications systems the channel estimation filter suppresses the additive noise in channel estimates obtained from pilot symbols. Vehicular channels show local stationarity for a finite region in time and frequency, only. Such a process can be divided into consecutive stationarity regions, allowing to calculate a Wiener filter. We analyze the increase of the mean square error (MSE) when using a mismatched Wiener filter calculated for a past stationarity region. The MSE increase of the filter is related to a new metric, the filter divergence directly observable at the receiver side. By accepting an increase of MSE, measured by the filter-divergence, the same filter coefficients can be used for several stationarity regions, allowing computational complexity reduction in a real system.}}, author = {{Bernadó, Laura and Zemen, Thomas and Paier, Alexander and Kåredal, Johan}}, booktitle = {{2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers}}, isbn = {{978-1-4244-9721-8}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Complexity reduction for vehicular channel estimation using the filter-divergence measure}}, url = {{http://dx.doi.org/10.1109/ACSSC.2010.5757485}}, doi = {{10.1109/ACSSC.2010.5757485}}, year = {{2011}}, }