Optimization of the second order autoregressive ...
Document type :
Communication dans un congrès avec actes
Title :
Optimization of the second order autoregressive model AR(2) for Rayleigh-Jakes flat fading channel estimation with Kalman filter
Author(s) :
El Husseini, Ali Houssam [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Simon, Eric [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]
Simon, Eric [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 :
DSP 2017 - 22th IEEE International Conference on Digital Signal Processing
Conference organizers(s) :
IEEE
City :
Londres
Country :
Royaume-Uni
Start date of the conference :
2017-08-23
Book title :
Digital Signal Processing
Publication date :
2017-08-23
English keyword(s) :
Rayleigh channel estimation
Autoregressive model
Jakes Doppler spectrum
Flat fading
Kalman filter
steady-state
Autoregressive model
Jakes Doppler spectrum
Flat fading
Kalman filter
steady-state
HAL domain(s) :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Mathématiques [math]/Théorie de l'information et codage [math.IT]
Mathématiques [math]/Théorie de l'information et codage [math.IT]
English abstract : [en]
This paper deals with the estimation of the flat fading Rayleigh channel with Jakes' Doppler spectrum (model due to R.H. Clarke in 1968) and slow fading variations. A common method in literature consists in approximating ...
Show more >This paper deals with the estimation of the flat fading Rayleigh channel with Jakes' Doppler spectrum (model due to R.H. Clarke in 1968) and slow fading variations. A common method in literature consists in approximating the variations of the channel using an auto-regressive model of order p (AR(p)), whose parameters are adjusted according to the " correlation matching " (CM) criterion and then estimated by a Kalman filter (KF). Recent studies based on first order AR(1) showed that the performance is far from the Bayesian Cramer-Rao bound for slow to moderate channel variations, which is the case for many applications. The same studies on first order model have shown the importance of replacing the CM criterion with a MAV criterion (Minimization of Asymptotic Variance). Moreover, it has been shown in literature that increasing the order of the model by passing from AR(1) to AR(2) did not improve the performance when the CM criterion is considered. In order to obtain an improvement in performance, it is essential to consider the MAV criterion with second order autoregressive model AR(2), as shown in this article. By imposing a linear relation between one of the parameters and the Doppler frequency, we derive analytic formulas for suboptimal adjustment of the parameters of AR(2) as a function of the noise level and the Doppler frequency of the channel. Theoretical assumptions are validated via simulation.Show less >
Show more >This paper deals with the estimation of the flat fading Rayleigh channel with Jakes' Doppler spectrum (model due to R.H. Clarke in 1968) and slow fading variations. A common method in literature consists in approximating the variations of the channel using an auto-regressive model of order p (AR(p)), whose parameters are adjusted according to the " correlation matching " (CM) criterion and then estimated by a Kalman filter (KF). Recent studies based on first order AR(1) showed that the performance is far from the Bayesian Cramer-Rao bound for slow to moderate channel variations, which is the case for many applications. The same studies on first order model have shown the importance of replacing the CM criterion with a MAV criterion (Minimization of Asymptotic Variance). Moreover, it has been shown in literature that increasing the order of the model by passing from AR(1) to AR(2) did not improve the performance when the CM criterion is considered. In order to obtain an improvement in performance, it is essential to consider the MAV criterion with second order autoregressive model AR(2), as shown in this article. By imposing a linear relation between one of the parameters and the Doppler frequency, we derive analytic formulas for suboptimal adjustment of the parameters of AR(2) as a function of the noise level and the Doppler frequency of the channel. Theoretical assumptions are validated via simulation.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Source :
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