Top

Published in:

2020 | OriginalPaper | Chapter

13. Blind Adaptive Filtering

Author : Paulo S. R. Diniz

Published in: Adaptive Filtering

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

There are a number of applications where the reference signal is either not available or consists of a training signal that in communication systems implies in reduction of useful data transmission. In those cases, we should utilize some alternative objective functions applied to the available data as well as some knowledge related to the nature (properties) of the signals involved.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Subband Adaptive Filters

next chapter Kalman Filters

In the ZF solution, the equalized signal is forced to be equal to the transmitted signal, a solution not recommended whenever the environment noise is not negligible, due to noise enhancement. The ZF equalizer aims at estimating a channel inverse in order to eliminate intersymbol interference.

Channels whose discrete-time models have poles and zeros outside the unit circle.

The M-ary PAM constellation points are represented by \(s_{i} = \tilde{a}_{i}\), with \(\tilde{a}_{i} = \pm \tilde{d}, \pm 3 \tilde{d}, \ldots , \pm (\sqrt{M}-1) \tilde{d}\). The parameter \(\tilde{d}\) represents half of the distance between two points in the constellation.

A solution exists but it is not unique.

A constant value multiplying the channel model.

In comparison with the algorithms using high-order statistics explicitly.

Z. Ding, Y. Li, Blind Equalization and Identification (Marcel Dekker, New York, 2001)CrossRef

S. Haykin (ed.), Unsupervised Adaptive Filtering, Vol. I: Blind Source Separation (Wiley, New York, 2000)

J.M.T. Romano, R.R.F. Attux, C.C. Cavalcante, R. Suyama, Unsupervised Signal Processing: Channel Equalization and Source Separation (CRC, Boca Raton, 2011)

S. Haykin (ed.), Unsupervised Adaptive Filtering, Vol. II: Blind Deconvolution (Wiley, New York, 2000)

C.-Y. Chi, C.-C. Feng, C.-H. Chen, C.-Y. Chen, Blind Equalization and System Identification (Springer, London, 2006)

J.R. Barry, E.A. Lee, D.G. Messerschmitt, Digital Communication, 3rd edn. (Kluwer Academic, Boston, 2004)CrossRef

C.L. Nikias, A.P. Petropulu, Higher-Order Spectra Analysis: A Nonlinear Signal Processing Framework (Prentice Hall, Englewood Cliffs, 1993)MATH

Y. Sato, A method of self-recovering equalization for multi-level amplitude modulation. IEEE Trans. Commun. (COM) 23, 679–682 (1975)CrossRef

A. Benveniste, M. Gousat, R. Ruget, Robust identification nonminimum phase system: Blind adjustment of a linear equalizer in data communications. IEEE Trans. Automat. Control (AC) 25, 385–399 (1980)

10.

D.N. Godard, Self-recovering equalization and carrier tracking in two-dimensional data communication system. IEEE Trans. Commun. (COM) 28, 1867–1875 (1980)CrossRef

11.

C.B. Papadias, D.T.M. Slock, Normalized sliding window constant-modulus and decision-directed algorithms: a link between blind equalization and classical adaptive filtering. IEEE Trans. Signal Process. 45, 231–235 (1997)CrossRef

12.

E. Moulines, P. Duhamel, J.-F. Cardoso, S. Mayarargue, Subspace methods for the blind identification of multichannel FIR filters. IEEE Trans. Signal Process. 43, 516–525 (1995)CrossRef

13.

D. Gesbert, P. Duhamel, Unbiased blind adaptive channel identification. IEEE Trans. Signal Process. 48, 148–158 (2000)CrossRef

14.

X. Li, H. Fan, Direct estimation of blind zero-forcing equalizers based on second-order statistics. IEEE Trans. Signal Process. 48, 2211–2218 (2000)CrossRef

15.

L. Tong, Q. Zhao, Jointly order detection and blind channel estimation by least squares smoothing. IEEE Trans. Signal Process. 47, 2345–2355 (1999)CrossRef

16.

A.P. Liavas, P.A. Regalia, J.-P. Delmas, Blind channel approximation: Effective channel order determination. IEEE Trans. Signal Process. 47, 3336–3344 (1999)CrossRef

17.

A.J. Viterbi, Principles of Spread Spectrum Communication (Addison Wesley, Reading, 1995)

18.

M. Honig, M.K. Tsatsanis, Adaptive techniques for multiuser CDMA receivers. IEEE Signal Process. Mag. 17, 49–61 (2000)CrossRef

19.

S. Verdu, Multiuser Detection (Cambridge University Press, Cambridge, 1998)MATH

20.

M.L. Honig, U. Madhow, S. Verdú, Blind adaptive multiuser detection. IEEE Trans. Inf. Theor. 41, 944–960 (1995)CrossRef

21.

M.K. Tsatsanis, Inverse filtering criteria for CDMA systems. IEEE Trans. Signal Process. 45, 102–112 (1997)CrossRef

22.

Z. Xu, M.K. Tsatsanis, Blind adaptive algorithms for minimum variance CDMA receivers. IEEE Trans. Signal Process. 49, 180–194 (2001)MATH

23.

Z. Xu, P. Liu, X. Wang, Blind multiuser detection: From MOE to subspace methods. IEEE Trans. Signal Process. 52, 510–524 (2004)MathSciNetCrossRef

24.

X. Wang, H.V. Poor, Wireless Communication Systems: Advanced Techniques for Signal Reception (Prentice Hall, Upper Saddle River, 2003)

25.

E. de Carvalho, D.T.M. Slock, Blind and semi-blind FIR multichannel estimation: (Global) Identifiability conditions. IEEE Trans. Signal Process. 52, 1053–1064 (2004)MathSciNetCrossRef

26.

A. Paulraj, R. Nabar, D. Gore, Introduction to Space-Time Wireless Communications (Cambridge University Press, Cambridge, 2003)

27.

E.G. Larsson, P. Stoica, Space-Time Block Coding for Wireless Communications (Cambridge University Press, Cambridge, 2003)CrossRef

28.

A. Hottinen, O. Tirkkonen, R. Wichman, Multi-Antenna Transceiver Techniques for 3G and Beyond (Wiley, New York, 2003)CrossRef

29.

H.L. Van Trees, Optimum Array Processing: Detection, Estimation, and Modulation Theory, Part IV (Wiley Interscience, New York, 2002)CrossRef

30.

L. Tong, Z. Ding, Single-user channel estimation and equalization. IEEE Signal Process. Mag. 17, 17–28 (2000)

31.

L. Tong, G. Xu, T. Kailath, Blind identification and equalization based on second-order statistics: a time domain approach. IEEE Trans. Inf. Theor. 40, 340–349 (1994)CrossRef

32.

L. Tong, G. Xu, B. Hassibi, T. Kailath, Blind identification and equalization based on second-order statistics: a frequency-domain approach. IEEE Trans. Inf. Theor. 41, 329–334 (1994)CrossRef

33.

E. Serpedin, G.B. Giannakis, A simple proof of a known blind channel identifiability result. IEEE Trans. Signal Process. 47, 591–593 (1999)CrossRef

34.

Y. Li, Z. Ding, Global convergence of fractionally spaced Godard (CMA) adaptive equalizers. IEEE Trans. Signal Process. 44, 818–826 (1996)CrossRef

35.

R.C. de Lamare, P.S.R. Diniz, Blind constrained set-membership algorithms with time-varying bounds for CDMA interference suppression, in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toulouse, France, May 2006, pp. IV-617–IV-620

Title: Blind Adaptive Filtering
Author: Paulo S. R. Diniz
Publisher: Springer International Publishing
Book: Adaptive Filtering
Print ISBN: 978-3-030-29056-6

Electronic ISBN: 978-3-030-29057-3

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-29057-3_13