Compressed sensing block MAP-LMS adaptive filter for sparse channel estimation and a bayesian Cramer-Rao bound

Hadi Zayyani (1) , Massoud Babaie-Zadeh (1) , Christian Jutten (2)

1 EE department of SUT [Tehran] - Department of Electrical Engineering [Tehran]
2 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique

This paper suggests to use a Block MAP-LMS (BMAPLMS) adaptive filter instead of an Adaptive Filter called MAP-LMS for estimating the sparse channels. Moreover to faster convergence than MAP-LMS, this block-based adaptive filter enables us to use a compressed sensing version of it which exploits the sparsity of the channel outputs to reduce the sampling rate of the received signal and to alleviate the complexity of the BMAP-LMS. Our simulations show that our proposed algorithm has faster convergence and less final MSE than MAP-LMS, while it is more complex than MAP-LMS. Moreover, some lower bounds for sparse channel estimation is discussed. Specially, a Cramer-Rao bound and a Bayesian Cramer-Rao bound is also calculated.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten. Compressed sensing block MAP-LMS adaptive filter for sparse channel estimation and a bayesian Cramer-Rao bound. MLSP 2009 - IEEE 19th International Workshop on Machine Learning for Signal Processing, Sep 2009, Grenoble, France. 6 p. ⟨hal-00424165⟩