Improved bayesian MIMO channel tracking for wireless communications: incorporating a dynamical model

Authors:
K. Huber;S. Haykin
Affiliations:
Array Syst. Comput. Inc., Ont.;-
Venue:
IEEE Transactions on Wireless Communications
Year:
2006

Citing 0
Cited 3

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Abstract

This paper investigates the improved decoder performance offered by incorporating dynamic linear modelling techniques when applied to particle filters for use in tracking the MIMO wireless channel. Conventional Bayesian-based receivers that perform channel tracking necessarily require a wireless channel model, typified by the use of a low order auto-regressive (AR) model. Normally, the model parameters are static in nature and are estimated a priori of any transmission; thus if the channel conditions change, a model mismatch occurs and system performance is degraded. Our method allows for time-varying channel statistics by modelling the channel fading rate as a Markov random walk. This new procedure allows the channel model to assume a time-varying behavior. As shown through simulations, the incorporation of dynamic modelling of time-dispersive channels not only offers superior performance, but at high SNR eliminates the error-rate floor commonly seen in systems using the static AR models