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

• Published in: 2009 IEEE International Workshop on Machine Learning for Signal Processing pp. 1 - 6
• Main Authors: Zayyani, H., Babaie-Zadeh, M., Jutten, C.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2009
• Subjects: Adaptive filters; Bayesian methods; Channel estimation; Collaborative work; Compressed sensing; Convergence; Geophysical measurements; Matching pursuit algorithms; Sampling methods; Signal processing algorithms
• ISBN: 1424449472; 9781424449477
• ISSN: 1551-2541; 2378-928X
• DOI: 10.1109/MLSP.2009.5306268

This paper suggests to use a block MAP-LMS (BMAP-LMS) 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.