Errors-In-Variables-Based Approach for the Identification of AR Time-Varying Fading Channels

dc.contributor.author Jamoos, Ali
dc.contributor.author Grivel, Eric
dc.contributor.author Bobillet, William
dc.contributor.author Guidorzi, Roberto
dc.date.accessioned 2020-09-28T09:07:01Z
dc.date.available 2020-09-28T09:07:01Z
dc.date.issued 2007-11
dc.description.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. en_US
dc.identifier.uri https://dspace.alquds.edu/handle/20.500.12213/6144
dc.language.iso en_US en_US
dc.publisher IEEE SIGNAL PROCESSING LETTERS en_US
dc.subject Autoregressive processes en_US
dc.subject errors-in-variables en
dc.subject Rayleigh fading channels en
dc.title Errors-In-Variables-Based Approach for the Identification of AR Time-Varying Fading Channels en_US
dc.type Article en_US
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