Abstract
Recently, there has been a growing interest in estimation of sparse channels as they are observed in underwater acoustic and ultrawideband channels. In this paper we present a new Bayesian sparse channel estimation (SCE) algorithm that, unlike traditional SCE methods, exploits noise statistical information to improve the estimates. The proposed method uses approximate maximum a posteriori probability (MAP) to detect the non-zero channel tap locations while least square estimation is used to determine the values of the channel taps. Computer simulations shows that the proposed algorithm outperforms the existing algorithms in terms of normalized mean squared error (NMSE) and approaches Cramér-Rao lower bound of the estimation. In addition, it has low computational cost when compared to the other algorithms.
C. Jutten—This work has been partly funded by ERC project 2012-ERC-AdG-320684 CHESS.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kilfoyle, D.B., Baggeroer, A.B.: The state of the art in underwater acoustic telemetry. IEEE J. Oceanic Eng. 25(1), 4–27 (2000)
Molisch, A.F.: Ultrawideband propagation channels-theory, measurement, and modeling. IEEE Trans. Veh. Technol. 54(5), 1528–1545 (2005)
Claerbout, J.F., Muir, F.: Robust modeling with erratic data. Geophysics 38(5), 826–844 (1973)
Carbonelli, C., Vedantam, S., Mitra, U.: Sparse channel estimation with zero tap detection. IEEE Trans. Wirel. Commun. 6(5), 1743–1763 (2007)
Niazadeh, R., Babaie-Zadeh, M., Jutten, C.: An alternating minimization method for sparse channel estimation. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds.) LVA/ICA 2010. LNCS, vol. 6365, pp. 319–327. Springer, Heidelberg (2010)
Subramanian, S.: Compressed sensing for sparse underwater channel estimation: Some practical considerations (2010). arXiv preprint arXiv:1002.2677
Sharp, M., Scaglione, A.: Estimation of sparse multipath channels. In: Military Communications Conference, MILCOM, pp. 1–7. IEEE (2008)
Cotter, S.F., Rao, B.D.: Sparse channel estimation via matching pursuit with application to equalization. IEEE Trans. Commun. 50(3), 374–377 (2002)
Karabulut, G.Z., Yongacoglu, A.: Sparse channel estimation using orthogonal matching pursuit algorithm. In: Vehicular Technology Conference, VTC, vol. 6, pp. 3880–3884. IEEE (2004)
Wan, F., Mitra, U., Molisch, A.: The modified iterative detector/estimator algorithm for sparse channel estimation. In: OCEANS, pp. 1–6. IEEE (2010)
Masood, M., Al-Naffouri, T.Y.: Sparse reconstruction using distribution agnostic Bayesian matching pursuit. IEEE Trans. Sig. Process. 61(21), 5298–5309 (2013)
Rao, C.R., Toutenburg, H., Shalabh, H.C., Schomaker, M.: Linear Models and Generalizations: Least Squares and Alternatives, 3rd edn. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Daei, S., Babaie-Zadeh, M., Jutten, C. (2015). A MAP-Based Order Estimation Procedure for Sparse Channel Estimation. In: Vincent, E., Yeredor, A., Koldovský, Z., Tichavský, P. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2015. Lecture Notes in Computer Science(), vol 9237. Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-22482-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22481-7
Online ISBN: 978-3-319-22482-4
eBook Packages: Computer ScienceComputer Science (R0)