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Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments. (English) Zbl 1395.93544
Summary: Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsity-aware channel estimation algorithms have been developed based on the well-known minimum mean square error (MMSE) criterion, such as the zero-attracting least mean square (ZALMS),which are robust under Gaussian assumption. In non-Gaussian environments, however, these methods are often no longer robust especially when systems are disturbed by random impulsive noises. To address this problem, we propose in this work a robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation. Specifically, MCC is utilized to mitigate the impulsive noise while CIM is adopted to exploit the channel sparsity efficiently. Both theoretical analysis and computer simulations are provided to corroborate the proposed methods.

93E11 Filtering in stochastic control theory
93E10 Estimation and detection in stochastic control theory
93B35 Sensitivity (robustness)
94A12 Signal theory (characterization, reconstruction, filtering, etc.)
93D21 Adaptive or robust stabilization
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[1] Davis, L. M.; Hanly, V. S.; Tune, P.; Bhaskaran, S. R., Channel estimation and user selection in the MIMO broadcast channel, Digit. Signal Process., 21, 5, 608-618, (2011)
[2] G. Gui, L. Dai, S. Kumagai, and F. Adachi, Variable earns profit: improved adaptive channel estimation using sparse VSS-NLMS algorithms, in: Proceedings of the IEEE International Conference on Communications (ICC), Sydney, Australia, 2014, pp. 4390-4394.
[3] Rontogiannis, A. A.; Berberidis, K., Efficient decision feedback equalization for sparse wireless channels, IEEE Trans. Wirel. Commun., 2, 3, 570-581, (2003)
[4] Singer, A. C.; Nelson, J. K.; Kozat, S. S., Signal processing for underwater acoustic communications, IEEE Commun. Mag., 47, 1, 90-96, (2009)
[5] Cotter, S.; Rao, B., Sparse channel estimation via matching pursuit with application to equalization, IEEE Trans. Commun., 50, 3, 374-378, (2002)
[6] Yousef, N. R.; Sayed, A. H.; Khajehnouri, N., Detection of fading overlapping multipath components, Signal Process., 86, 9, 2407-2425, (2006) · Zbl 1172.94536
[7] Duttweiler., D. L., Proportionate normalized least-mean-squares adaptation in echo cancellers, IEEE Trans. Speech Audio Process., 8, 5, 508-518, (2000)
[8] J. Benesty, S.L. Gay, An improved PNLMS algorithm, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2002, pp. 1881-1884.
[9] Abadi, M. S.E.; Kadkhodazadeh, S., A family of proportionate normalized subband adaptive filter algorithms, J. Frankl. Inst., 348, 2, 212-238, (2011) · Zbl 1208.94026
[10] Tibshirani, R., Regression shrinkage and selection via the lasso, J. R. Stat. Soc. B, 24, 4, 21-30, (2007)
[11] Baraniuk, R. G., Compressive sensing, IEEE Signal Process. Mag., 24, 4, 21-30, (2007)
[12] Y. Chen, Y. Gu, A.O. Hero, Sparse L.M.S., for system identification, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2009, pp. 3125-3128.
[13] Babadi, B.; Kalouptsidis, N.; Tarokh, V., SPARLS: the sparse RLS algorithm, IEEE Trans. Signal Process., 8, 8, 4013-4025, (2010) · Zbl 1392.94080
[14] O. Taheri, S.A. Vorobyov, Sparse channel estimation with lp-norm and reweighted l1-norm penalized least mean squares, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011, pp. 2864-2867.
[15] Wu, F. Y.; Tong, F., Gradient optimization lp-norm-like constraint LMS algorithm for sparse system estimation, Signal Process., 93, 4, 967-971, (2013)
[16] M.L. Aliyu, M.A. Alkassim, M.S. Salman, A p-norm variable step-size LMS algorithm for sparse system identification. In: E. Cetin (Ed.), Signal Image and Video Processing, Springer, London, http://dx.doi.org/10.1007/s11760-013-0610-7, 2013
[17] G. Gui, W. Peng, F. Adachi, Improved adaptive sparse channel estimation based on the least mean square algorithm, in: Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), 2013, pp. 3105-3109.
[18] G. Gui, A. Mehbodniya, F. Adachi, Least mean square/fourth algorithm for adaptive sparse channel estimation, in: Proceedings of the IEEE 24th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 2013, pp. 296-300.
[19] Brockett, P. L.; Hinich, M.; Wilson, G. R., Nonlinear and non-gaussian Ocean noise, J. Acoust. Soc. Am., 82, 4, 1386-1394, (1987)
[20] A.T. Georgiadis, B. Mulgrew, A family of recursive algorithms for channel identification in alpha-stable noise, in: Proceedings of the Fifth Bayona Workshop on Emerging Technologies in Telecommunications, 1999, pp. 153-157.
[21] Weng, B.; Barner, K. E., Nonlinear system identification in impulsive environments, IEEE Trans. Signal Process., 53, 7, 2588-2594, (2005) · Zbl 1370.93302
[22] Wen, F., Diffusion least-mean p-power algorithms for distributed estimation in alpha-stable noise environments, Electron. Lett., 49, 21, 1355-1356, (2013)
[23] Liu, W.; Pokharel, P.; Principe, J. C., Correntropy: properties and applications in non-Gaussian signal processing, IEEE Trans. Signal Process., 55, 11, 5286-5298, (2007) · Zbl 1390.94277
[24] Chen, B.; Xing, L.; Zheng, N.; Principe, J. C., Steady-state mean square error analysis for adaptive filtering under the maximum correntropy criterion, IEEE Signal Process. Lett., 21, 7, 880-884, (2014)
[25] A. Singh, J. C. Principe, Using correntropy as cost function in adaptive filters, in: Proceedings of International Joint Conference on Neural Networks, 2009, pp. 2950-2955.
[26] S. Zhao, B. Chen, J. C. Principe, Kernel adaptive filtering with maximum Correntropy criterion, in: Proceedings of International Joint Conference on Neural Networks, 2011, pp. 2012-2017.
[27] Chen, B.; Príncipe, J. C., Maximum correntropy estimation is a smoothed MAP estimation, IEEE Signal Process. Lett., 19, 8, 491-494, (2012)
[28] X. Yuan, B.G..Hu, Robust feature extraction via information theoretic learning, in: Proceedings of the 26th Annual International Conference on Machine Learning, 2009, pp. 1193-1200.
[29] He, R.; Zheng, W. S.; Hu, B. G., Maximum correntropy criterion for robust face recognition, IEEE Trans. Pattern Anal. Mach. Intell., 20, 6, 1485-1494, (2011) · Zbl 1372.94369
[30] He, R.; Hu, B. G.; Zheng, W. S.; Kong, X. W., Robust principal component analysis based on maximum correntropy criterion, IEEE Trans. Image Process., 20, 6, 1485-1494, (2011) · Zbl 1372.94369
[31] Jin, J.; Gu, Y.; Mei, S., Stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework, IEEE J. Sel. Top. Signal Process., 4, 2, 409-420, (2010)
[32] S. Seth, J.C. Príncipe, Compressed signal reconstruction using the correntropy induced metric, in: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2008, pp. 3845-3848.
[33] Shi, K.; Shi, P., Convergence analysis of sparse LMS algorithms with l_{1}-norm penalty based on white input signal, Signal Process., 90, 12, 3289-3293, (2010) · Zbl 1197.94124
[34] Salman, M. S., Sparse leaky-LMS algorithm for system identification and its convergence analysis, Int. J. Adapt. Control Signal Process., 28, 10, 1065-1072, (2014) · Zbl 1337.93093
[35] M.S. Jahromi, M.S. Salman, A. Hocanin, Convergence analysis of the zero-attracting variable step-size LMS algorithm for sparse system identification. In: E. Cetin (Ed.), Signal Image and Video Processing. Springer London, 10.1007/s11760-013-0580-9, 2014.
[36] Pelekanakis, K.; Chitre, M., Adaptive sparse channel estimation under symmetric alpha-stable noise, IEEE Trans. Wirel. Commun., 13, 6, 3183-3195, (2014)
[37] Adler, R.; Feldman, R.; Taqqu, M. S., A practical guide to heavy tails statistical techniques for analyzing heavy-tailed distributions, (1997), Birkhauser Boston
[38] Zimmermann, M.; Dostert, K., Analysis and modeling of impulsive noise in broad-band power line communications, IEEE Trans. Electromagn. Compat., 44, 1, 249-258, (2002)
[39] Zhidkov, S. V., Impulsive noise suppression in OFDM-based communication systems, IEEE Trans. Consum. Electron., 49, 4, 944-948, (2003)
[40] Middleton, D., Non-Gaussian noise models in signal processing for telecommunications: new methods and results for class A and class B noise models, IEEE Trans. Inf. Theory, 45, 4, 1129-1149, (1999) · Zbl 0959.94004
[41] Shao, M.; Nikias, C. L., Signal processing with fractional lower order moments: stable processes and their applications, Proc. IEEE, 81, 7, 986-1010, (1993)
[42] Arenas-García, J.; Figueiras-Vidal, A. R., Adaptive combination of proportionate filters for sparse echo cancellation, IEEE Trans. Audio Speech Lang. Process., 17, 6, 1087-1098, (2009)
[43] Naylor, P. A.; Cui, J.; Brookes, M., Adaptive algorithms for sparse echo cancellation, Signal Process., 86, 6, 1182-1192, (2006) · Zbl 1163.94366
[44] A. Gonzalo-Ayuso, M.T.M. Silva, V.H. Nascimento, et. al., Improving sparse echo cancellation via convex combination of two NLMS filters with different lengths, in: Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2012, pp. 1-6.
[46] NRSC A.M., Preemphasis/Deemphasis and broadcast audio transmission bandwidth specifications (ANSI/EIA-549-88), Standard ANSI/EIA-549-88, 1988.
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