Maximum likelihood blind deconvolution for sparse systems

2010 2nd International Workshop on Cognitive Information Processing, CIP2010

Volumn , Issue , 2010, Pages 69-74

  Barembruch, Steffen ^a   Scaglione, Anna ^b   Moulines, Eric ^a  

^a TELECOM PARISTECH   (France)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Compressive Sensing; Deconvolution; Multipath channels; Smoothing methods

Indexed keywords

BLIND DECONVOLUTION; BLIND IDENTIFICATIONS; CHANNEL IDENTIFICATION; COMPRESSED SENSING; COMPRESSIVE SENSING; DIGITAL COMMUNICATIONS; DOUBLY-SELECTIVE CHANNELS; EM ALGORITHMS; LINEAR MODULATIONS; MULTIPATH CHANNELS; SMOOTHING METHODS; SPARSE ESTIMATION; SPARSE SYSTEMS; SPARSITY CONSTRAINTS; TRAINING SEQUENCES;

COMMUNICATION; CONVOLUTION; DATA PROCESSING; DIGITAL COMMUNICATION SYSTEMS; MULTIPATH PROPAGATION; SIGNAL RECONSTRUCTION;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 78349299845     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIP.2010.5604139     Document Type: Conference Paper

References (21)

1. E.J. Candes and T. Tao, "Decoding by linear programming," Information Theory, IEEE Transactions on, vol. 51, no. 12, pp. 4203-4215, Dec. 2005.
2. E.J. Candes and T. Tao, "Near-optimal signal recovery from random projections: Universal encoding strategies?," Information Theory, IEEE Transactions on, vol. 52, no. 12, pp. 5406-5425, Dec. 2006.
3. S.G. Mallat and Zhifeng Zhang, "Matching pursuits with time-frequency dictionaries," Signal Processing, IEEE Trans. on, vol. 41, no. 12, pp. 3397-3415, 1993.
4. Y.C. Pati, R. Rezaiifar, and P.S. Krishnaprasad, "Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition," in Signals, Systems and Computers, 1993. The 27th Asilomar Conf. on, Nov 1993, pp. 40-44 vol.1.
5. W.U. Bajwa, J. Haupt, G. Raz, and R. Nowak, "Compressed channel sensing," in Information Sciences and Systems, 2008. CISS 2008. 42nd Annual Conference on, March 2008, pp. 5-10.
6. S.F. Cotter and B.D. Rao, "Sparse channel estimation via matching pursuit with application to equalization," Communications, IEEE Transactions on, vol. 50, no. 3, pp. 374-377, Mar 2002.
7. W.U. Bajwa, A.M. Sayeed, and R. Nowak, "Learning sparse doubly-selective channels," in Communication, Control, and Computing, 46th Annual Allerton Conference on, Sept. 2008, pp. 575-582.
8. G. Tauböck and F. Hlawatsch, "Compressed sensing based estimation of doubly selective channels using a sparsity-optimized basis expansion," in EUSIPCO 2008, (Lausanne, Switzerland), 2008.
9. G.B. Giannakis and C. Tepedelenlioglu, "Basis expansion models and diversity techniques for blind identification and equalization of time-varying channels," Proceedings of the IEEE, vol. 86, no. 10, pp. 1969-1986, Oct 1998.
10. F. Ben Salem and G. Salut, "Deterministic particle receiver for multipath fading channels in wireless communications. part I: FDMA," Traitement du Signal, vol. 21, no. 4, pp. 347-358, 2004.
11. A. P. Dempster, N. M. Laird, and D. B. Rubin, "Maximum-likelihood from incomplete data via the EM algorithm," Journal of the Royal Statistical Society, vol. 39(1), pp. 1-38, 1977.
12. W. Turin, "MAP decoding in channels with memory," Communications, IEEE Transactions on, vol. 48, no. 5, pp. 757-763, May 2000.
13. C.N. Georghiades and Jae Choong Han, "Sequence estimation in the presence of random parameters via the em algorithm," Communications, IEEE Transactions on, vol. 45, no. 3, pp. 300-308, Mar 1997.
14. L.E. Baum, T. Petrie, G. Soules, and N. Weiss, "A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains," The Annals of Mathematical Statistics, vol. 41, pp. 164-171, 1970.
15. M. Sharp and A. Scaglione, "Estimation of sparse multipath channels," in Proc. IEEE Military Communications Conference (MILCOM), November 2008, pp. 1-7.
16. O. Cappé, E. Moulines, and T. Rydén, Inference in Hidden Markov Models, Springer, 2nd edition edition, 2007.
17. A. Doucet, S. Godsill, and C. Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering," Statistics and Computing, vol. 10, no. 3, pp. 197-208, 2000.
18. P. Fearnhead, D. Wyncoll, and J. Tawn, "A sequential smoothing algorithm with linear computational cost," submitted, 2008.
19. S. Barembruch, A. Garivier, and E. Moulines, "On approximate maximum likelihood methods for blind identification: How to cope with the curse of dimensionality," IEEE Transactions on Signal Processing, vol. 57, no. 11, pp. 4247 - 4259, 2009.
20. R. Tibshirani, "Regression shrinkage and selection via the Lasso," J. Royal. Statist. Soc B., vol. 58, pp. 267-288, 1996.
21. D. L. Donoho, "For most large underdetermined systems of equations, the minimal L1-norm near-solution approximates the sparsest near-solution," Comm. Pure Appl. Math, vol. 59, pp. 907-934, 2006.