On the Use of First-order Autoregressive Modeling for Rayleigh Flat Fading Channel Estimation with Kalman FilterReport as inadecuate




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1 GIPSA-CICS - CICS GIPSA-DIS - Département Images et Signal

Abstract : This letter deals with the estimation of a flat fading Rayleigh channel with Jakes-s spectrum. The channel is approximated by a first-order autoregressive AR1 model and tracked by a Kalman Filter KF. The common method used in the literature to estimate the parameter of the AR1 model is based on a Correlation Matching CM criterion. However, for slow fading variations, another criterion based on the Minimization of the Asymptotic Variance MAV of the KF is more appropriate, as already observed in few works 1. This letter gives analytic justification by providing approximated closed-form expressions of the estimation variance for the CM and MAV criteria, and of the optimal AR1 parameter.

Keywords : Bayesian Cramér-Rao Bounds BCRB Bayesian Cramér-Rao Bounds BCRB. Flat fading Rayleigh channel Jakes-s spectrum Kalman Filter Autoregressive model Channel estimation





Author: Soukayna Ghandour - Haidar - Laurent Ros - Jean-Marc Brossier -

Source: https://hal.archives-ouvertes.fr/



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