Fast adaptive digital equalization by recurrent neural networks

IEEE Transactions on Signal Processing

Volumn 45, Issue 11, 1997, Pages 2731-2739

  Parisi, Raffaele ^a   Di Claudio, Elio D ^a   Orlandi, Gianni ^a   Rao, Bhaskar D ^a  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BIT ERROR RATE; COMMUNICATION CHANNELS (INFORMATION THEORY); CONVERGENCE OF NUMERICAL METHODS; DIGITAL COMMUNICATION SYSTEMS; FEEDFORWARD NEURAL NETWORKS; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; OPTIMIZATION; PULSE AMPLITUDE MODULATION;

MEAN SQUARE ERROR (MSE); RECURRENT NEURAL NETWORKS (RNN);

SIGNAL PROCESSING;

EID: 0031268142     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/78.650099     Document Type: Article

References (33)

1. P. J. Huber, Robust Statistics. New York: Wiley, 1981.
2. J. G. Proakis, Digital Communications. New York: McGraw-Hill, 1983.
3. A. Papoulis, Probability, Random Variables, and Stochastic Processes, 2nd ed. New York: McGraw-Hill, 1984.
4. S. U. H. Qureshi, "Adaptive equalization," Proc. IEEE, vol. 73, Sept. 1985.
5. D. E. Rumelhart, G. E. Hinton, and R. G. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing: Exploration in the Microstructure of Cognition, D. E. Rumelhart and J. L. McLelland, Eds. Cambridge, MA: MIT Press/Bradford, 1986, vol. 1.
6. D. G. Luenberger, Linear and Nonlinear Programming. 2nd ed. Reading, MA: Addison-Wesley, 1989.
7. G. H. Golub and C. F. Van Loan, Matrix Computations. Baltimore, MD: Johns Hopkins Univ. Press, 1989.
8. R. J. Williams and D. Zipser, "A learning algorithm for continually running fully recurrent neural networks," Neural Comput., vol. 1, pp. 270-280, 1989.
9. G. Cybenko, "Approximations by superpositions of a sigmoidal function," Math. Contr. Signals Syst., vol. 2, no. 4, 1989.
10. L. P. Ammann, "Statistically robust signal subspace identification," in Proc. Int. Conf. Acoust., Speech, Signal Process., ICASSP, 1991, pp. 2711-2714.
11. R. J. Williams and J. Peng, "An efficient gradient-based algorithm for on-line training of recurrent network trajectories," Neural Comput., vol. 1, pp. 270-280.
12. J. L. Elman, "Finding structure in time," Cognitive Sei., vol. 14, pp. 179-211, 1990.
13. S. Chen, G. J. Gibson, C. F. N. Cowan, and P. M. Grant, "Adaptive equalization of finite nonlinear channels using multilayer perceptrons," Signal Process., vol. 20, pp. 107-119, 1990.
14. E. D. Di Claudio, G. Orlandi, F. Piazza, and A. Uncini, "Optimal weighted LS AR estimation in presence of impulsive noise," in Proc. Int. Conf. Acoust., Speech, Signal Process., ICASSP, May 1991, pp. 3149-3152.
15. G. J. Gibson, S. Siu, and C. F. N. Cowan, "The application of nonlinear structures to the reconstruction of binary signals," IEEE Trans. Signal Processing, vol. 39, Aug. 1991.
16. H. Leung and S. Haykin, "The complex backpropagation algorithm," IEEE Trans. Signal Processing, vol. 39, no. 9, Sept. 1991.
17. S. Haykin, Adaptive Filter Theory, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 1991.
18. Q. Zhang and A. Benveniste, "Wavelet networks," IEEE Trans. Neural Networks, vol. 3, Nov. 1992.
19. B. H. Juang and S. Katagiri, "Discriminative learning for minimum error classification," IEEE Trans. Signal Processing, vol. 40, Dec. 1992.
20. W. R. Kirkland and D. P. Taylor, "On the application of feedforward neural networks to channel equalization," in Proc. IJCNNInt. Joint Conf. Neural Networks, New York, 1992.
21. M. Peng, C. L. Nikias, and J. G. Proakis, "Adaptive equalization with neural networks: New multilayer perceptron structures and their evaluation," in Proc. ICASSP'92 IEEE Int. Conf. Acoust., Speech Signal Processing, New York, 1992.
22. S. Chen, B. Mulgrew, and S. McLaughlin, "Adaptive bayesian equalizer with decision feedback," IEEE Trans. Signal Processing, vol. 41, Sept. 1993.
23. S. Saarinen, R. Bramley, and G. Cybenko, "Ill-conditioning in neural network training problems," SIAM J. Sei. CompuL, vol. 14, no. 3, pp. 693-714, May 1993.
24. E. A. Lee and D. G. Messerschmitt, Digital Communication. Boston, MA: Kluwer, 1994.
25. S. Haykin, Neural Networks-A Comprehensive Foundation. New York: IEEE, 1994.
26. G. Kechriotis, E. Zervas, and E. S. Manolakos, "Using recurrent neural networks for adaptive communication channel equalization," IEEE Trans. Neural Networks, vol. 5, Mar. 1994.
27. P. R. Chang, B. F. Yeh, and C. C. Chang, "Adaptive packet equalization for indoor radio channel using multilayer neural networks," IEEE Trans. Veh. Technol, vol. 43, Aug. 1994.
28. P. R. Chang and B. F. Yeh, "Nonlinear communication channel equalization using wavelet neural networks," Proc. IEEE Int. Conf. Neural Networks, New York, 1994.
29. K. A. Al-Mashouq and I. S. Reed, "The use of neural nets to combine equalization with decoding for severe intersymbol interference channels," IEEE Trans. Neural Networks, vol. 5, Nov. 1994.
30. P. R. Chang and B. C. Wang, "Adaptive decision feedback equalization for digital satellite channels using multilayer neural networks," IEEE J. Select. Areas Commun., vol. 13, Feb. 1995.
31. B. A. Pearlmutter, "Gradient calculations for dynamic recurrent neural networks: a survey," IEEE Trans. Neural Networks, vol. 6, Sept. 1994.
32. R. Parisi, E. D. Di Claudio, A. Rapagnetta, and G. Orlandi, "Recursive least squares approach to learning in recurrent neural networks," in Proc. Int. Conf. Neural Networks ICNN, Washington, D.C., June 3-6, 1996.
33. R. Parisi, E. D. Di Claudio, G. Orlandi, and B. D. Rao, "A generalized learning paradigm exploiting the structure of neural networks," IEEE Trans. Neural Networks, vol. 7, Nov. 1996.