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

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

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