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Neural Computing and Applications

Erschienen in:

01.12.2005 | Original Article

Feedforward neural network for blind equalization with PSK signals

verfasst von: Rajoo Pandey

Erschienen in: Neural Computing and Applications | Ausgabe 4/2005

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Abstract

Most of the cost functions used for blind equalization are nonconvex and nonlinear functions of tap weights, when implemented using linear transversal filter structures. Therefore, a blind equalization scheme with a nonlinear structure that can form nonconvex decision regions is desirable. The efficacy of complex-valued feedforward neural networks for blind equalization of linear and nonlinear communication channels has been confirmed by many studies. In this paper we present a complex valued neural network for blind equalization with M-ary phase shift keying (PSK) signals. The complex nonlinear activation functions used in the neural network are especially defined for handling the M-ary PSK signals. The training algorithm based on constant modulus algorithm (CMA) cost function is derived. The improved performance of the proposed neural network in both, stationary and nonstationary environments, is confirmed through computer simulations.

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Amari SI, Cichocki A (1998) Adaptive blind signal processing–Neural network approaches. Proc IEEE 86(10):2026–2048CrossRef

Chen S, Gibson GJ, Cowan CFN, Grant PM (1990) Adaptive equalization of finite nonlinear channels using multilayer perceptrons. Signal Process 20:107–109CrossRef

Chow TWS, Fang Y (2001) Neural blind deconvolution of MIMO noisy channels. IEEE T Circuits Syst-I 48(1):116–120CrossRef

Lin H, Amin M (2003) A dual mode technique for improved blind equalization for QAM signals. IEEE Signal Process Lett 10(2):29–31CrossRef

Thirion MN, Moreau E (2002) Generalized criterion for blind multivariate signal equalization. IEEE Signal Process Lett 9(2):72–74CrossRef

Destro Filho JB, Favier G, Travassos Romano JM (1996) Neural networks for blind equalization. Proc IEEE Int Conf Globcom 1:196–200

Fang Y, Chow TWS, Ng KT (1999) Linear neural network based blind equalization. Signal Process 76(1):37–42CrossRef

Feng CC, Chi CY (1999) Performance of cumulant based inverse filters for blind deconvolution. IEEE Trans Signal Proces 47(7):1922–1935CrossRef

Choi S, Cichocki A (1998) Cascade neural networks for multichannel blind deconvolution. Electron Lett 34(12):1186–1187CrossRef

10.

Kechriotis G, Zervas E, Manolakos ES (1994) Using recurrent neural networks for adaptive communication channel equalization. IEEE T Neural Networ 5(2):267–278CrossRef

11.

Haykin S (1994) Blind deconvolution. Prentice-Hall, Englewood Cliffs

12.

Haykin S (1996) Adaptive filter theory, 3rd edn. Prentice Hall, Upper Saddel River

13.

You C, Hong D (1998) Nonlinear blind equalization schemes using complex valued multilayer feedforward neural networks. IEEE T Neural Networ 9(6):1442–1455CrossRef

14.

Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle River

15.

Haykin S (1996) Neural networks expand SP’s horizons. IEEE Signal Process Mag, pp 24–49

16.

Luo FL, Unbehauen R (1997) Applied neural networks for signal processing. Cambridge University Press, UK

17.

Cichocki A, Unbehauen R (1994) Neural networks for optimization and signal processing. Wiley, Chichester

18.

Benvenuto N, Piazza F (1992) On the complex backpropagation algorithm. IEEE T Signal Proces 40:967–969CrossRef

19.

Johnson CR et al (1998) Blind equalization using the constant modulus criterion : a review. Proc IEEE 86(10):1927–1950CrossRef

20.

Schniter P, Johnson CR Jr (1999) Dithered signed error CMA: robust, computationally efficient blind adaptive equalization. IEEE T Signal Proces 47(6):1592–1603CrossRef

21.

Jalon NK (1992) Joint blind equalization, carrier recovery and timing recovery for high order QAM constellations. IEEE T Signal Proces 40:1383–1398CrossRef

22.

Pandey R (2001) Blind equalization and signal separation using neural networks. PhD. thesis, I.I.T. Roorkee, India

23.

Liavas AP, Regalia PA, Delmas JP (1999) Blind channel approximation: effective channel order determination. IEEE T Signal Proces 47(12):3336–3344CrossRef

24.

Vesin JM, Gruter R (1999) Model selection using a simplex reproduction genetic algorithm. Signal Proces 78:321–327CrossRef

Titel: Feedforward neural network for blind equalization with PSK signals
verfasst von: Rajoo Pandey
Publikationsdatum: 01.12.2005
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 4/2005
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-004-0465-5

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