Kalman vs Hinf Algorithms for MC-DS-CDMA Channel Estimation With or Without A Priori AR Modeling

Abstract : This paper deals with the estimation of time-varying Multi-Carrier Direct-Sequence Code DivisionMultiple Access (MC-DS-CDMA) fading channels using a training-aided scheme. Our approach consists in using an optimal filtering based on a linear state-space model of the fading channel system. In that case, two issues have to be investigated: 1) what kind of optimal filtering can be used? 2) how to estimate the state-space matrices? Thus, Kalman filtering can be considered. It is optimal in the H2 sense providing the underlying state-space model is Gaussian and accurate. However, as these assumptions may no longer be satisfied in real cases, we propose to study the relevance of H∞ filtering. More particularly, when an explicit AR model is used for the channel, our first solution consists in estimating the fading channel and its AR parameters by means of two-cross-coupled H∞ filters. Instead of AR model based-estimators, our second contribution is to view the channel estimation as a realization issue where the state-space matrices are estimated by using subspace methods for system identification without any a priori explicit model for the channel.
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Contributor : Eric Grivel <>
Submitted on : Wednesday, August 22, 2007 - 10:57:06 AM
Last modification on : Thursday, January 11, 2018 - 6:21:07 AM


  • HAL Id : hal-00167677, version 1


Ali Jamoos, Eric Grivel, Julie Grolleau, Hanna Abdel-Nour. Kalman vs Hinf Algorithms for MC-DS-CDMA Channel Estimation With or Without A Priori AR Modeling. MC-SS (Multi Carrier Spread Spectrum), 2007, Herrsching, Germany. pp. 427-436. ⟨hal-00167677⟩



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