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Concurrent blind channel equalization with phase transmittance RBF neural networks
Journal of the Brazilian Computer Society volume 13, pages 18–25 (2007)
This paper presents a new complex valued radial basis function (RBF) neural network (NN) with phase transmittance between the input nodes and output, which makes it suitable for channel equalization on quadrature digital modulation systems. The new Phase Transmittance RBFNN (PTRBFNN) differs from the classical complex valued RBFNN in that it does not strictly rely on the Euclidean distance between the input vector and the center vectors, thus enabling the transference of phase information from input to output. In the context of blind channel equalization, results have shown that the PTRBFNN not only solves the phase uncertainty of the classical complex valued RBFNN but also presents a faster convergence rate.comes the abstract of the paper.
S. Chen, S. McLaughlin and B. Mulgrew: Complex-Valued Radial Basis Function Network, Part I: Network Architecture and Learning Algorithms.Signal Processing. vol. 35, pages 19–31,1994.
S. Chen, S. McLaughlin and B. Mulgrew: Complex-Valued Radial Basis Function Network, Part II: Application to Digital Communications Channel Equalisation.Signal Processing. vol. 36, pages 175–188, 1994.
Cha I. and Kassam S.: Channel Equalization Using Adaptative Complex Radial Basis Function Networks.IEEE Journal on Selected Areas in Communications. vol.13, no. 1, pages.122–131, 1995.
Gan Q., Saratchandran P., Sundararajan N., and Subramanian K. R.: A Complex Valued Radial Basis Function Network for Equalization of Fast Time Varying Channels.IEEE Transactions on Neural Networks. vol. 10, no. 4, 1999.
Jianping D., Sundararajan N. , and Saratchandran P.: Communication Channel Equalization Using Complex-Valued Minimal Radial Basis Function Neural Networks.IEEE Transactions on Neural Networks, vol. 13, no. 3, 2002.
J. G. Proakis. Digital Communications, 3rd ed., McGraw-Hill, 1995.
S. Haykin. Adaptative Filter Theory. 3rd ed., Prentice Hall, Upper Saddle River, New Jersey, 1996.
S. Haykin. Neural Networks. 2nd ed., Prentice Hall, Upper Saddle River, New Jersey, 1999.
S. Haykin. Blind Deconvolution. Prentice-Hall, 1994.
Fernando C.C. De Castro, M. Cristina F. De Castro, Dalton S. Arantes. Concurrent Blind Deconvolution for Channel Equalization.IEEE International Conference On Communications ICC2001. page. 366-371, Helsinki, Finland , June 2001.
Gitling R.D. and Weinstein S.B.: Fractionally-Spaced Equalization: An Improved Digital Transversal Equalizer.Bell Systems Technical Journal, vol. 60, 1981.
SPIB — Signal Processing Information Base. http://spib.rice.edu/spib/microwave.html, http://spib.rice.edu/spib/cable.html
Godard D.N.: Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems.IEEE Transactions on Communications. vol. COM-28, no.11, November 1980.
Chen S. and Chang E.S.: Fractionally spaced blind equalization with low-complexity concurrent constant modulus algorithm and soft decision-directed scheme.International Journal of Adaptive Control and Signal Processing. 19(6) page. 471–484, 2005.
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Loss, D.V., De Castro, M.C.F., Franco, P.R.G. et al. Concurrent blind channel equalization with phase transmittance RBF neural networks. J Braz Comp Soc 13, 18–25 (2007). https://doi.org/10.1007/BF03192398
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