Errors-In-Variables-Based Approach for the Identification of AR Time-Varying Fading Channels
Date
2007-11
Authors
Jamoos, Ali
Grivel, Eric
Bobillet, William
Guidorzi, Roberto
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE SIGNAL PROCESSING LETTERS
Abstract
This letter deals with the identification of time-varying
Rayleigh fading channels using a training sequence-based
approach. When the fading channel is approximated by an
autoregressive (AR) process, it can be estimated by means of
Kalman filtering, for instance. However, this method requires the
estimations of both the AR parameters and the noise variances in
the state–space representation of the system. For this purpose, the
existing noise compensated approaches could be considered, but
they usually require a long observation window and do not necessarily
provide reliable estimates when the signal-to-noise ratio is
low. Therefore, we propose to view the channel identification as an
errors-in-variables (EIV) issue. The method consists in searching
the noise variances that enable specific noise compensated autocorrelation
matrices of observations to be positive semidefinite. In
addition, the AR parameters can be estimated from the null spaces
of these matrices. Simulation results confirm the effectiveness of
this approach, especially in presence of a high amount of noise.
Description
Keywords
Autoregressive processes , errors-in-variables , Rayleigh fading channels