Channel Estimation for MIMO-OFDM Systems ...
Document type :
Communication dans un congrès avec actes
Title :
Channel Estimation for MIMO-OFDM Systems in Fast Time-Varying Environments
Author(s) :
Hijazi, Hussein [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Grenoble Images Parole Signal Automatique [GIPSA-lab]
Simon, Eric [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Lienard, Martine [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Ros, Laurent [Auteur]
GIPSA - Communication Information and Complex Systems [GIPSA-CICS]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Grenoble Images Parole Signal Automatique [GIPSA-lab]
Simon, Eric [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Lienard, Martine [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Ros, Laurent [Auteur]
GIPSA - Communication Information and Complex Systems [GIPSA-CICS]
Conference title :
ISCCSP 2008 - 4th International Symposium on Communications, Control and Signal Processing
City :
Limassol
Country :
Chypre
Start date of the conference :
2010-03-03
Book title :
Proceedings of the 4th International Symposium on Communications, Control and Signal Processing (ISCCSP 2010)
Publication date :
2010-03
English keyword(s) :
Basis Expansion Model (BEM)
MIMO systems
OFDM
time-varying Rayleigh channels
channel estimation
Kalman filter
Basis Expansion Model (BEM).
MIMO systems
OFDM
time-varying Rayleigh channels
channel estimation
Kalman filter
Basis Expansion Model (BEM).
HAL domain(s) :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
English abstract : [en]
A channel estimation algorithm for MIMO-OFDM systems in Fast Time-Varying Environments is proposed. The channel estimation function is based on the equivalent discrete-time channel taps or on the physical propagation channel ...
Show more >A channel estimation algorithm for MIMO-OFDM systems in Fast Time-Varying Environments is proposed. The channel estimation function is based on the equivalent discrete-time channel taps or on the physical propagation channel parameters. To handle rapid variations of channels within a transmission block, we approximate the channel by a basis expansion model (BEM). Based on the Jakes process, an auto-regressive (AR) model of the BEM coefficients dynamics is built, making it possible to estimate and track the BEM coefficients using Kalman filter. Hence, the channel matrix is easily computed, and the data symbol is detected with free ICI . Our claims are supported by theoretical analysis and simulation results, which are obtained considering Jakes' channels with high Doppler spreads.Show less >
Show more >A channel estimation algorithm for MIMO-OFDM systems in Fast Time-Varying Environments is proposed. The channel estimation function is based on the equivalent discrete-time channel taps or on the physical propagation channel parameters. To handle rapid variations of channels within a transmission block, we approximate the channel by a basis expansion model (BEM). Based on the Jakes process, an auto-regressive (AR) model of the BEM coefficients dynamics is built, making it possible to estimate and track the BEM coefficients using Kalman filter. Hence, the channel matrix is easily computed, and the data symbol is detected with free ICI . Our claims are supported by theoretical analysis and simulation results, which are obtained considering Jakes' channels with high Doppler spreads.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Source :
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