Joint nonlinear channel equalization and soft LDPC decoding with Gaussian processes

IEEE Transactions on Signal Processing

Volumn 58, Issue 3 PART 1, 2010, Pages 1183-1192

  Olmos, Pablo M ^a   Murillo Fuentes, Juan José ^a   Pérez Cruz, Fernando ^b,c  

^a UNIVERSIDAD DE SEVILLA   (Spain)

^b Princeton University   (United States)

^c UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

Coding; Equalization; Gaussian processes; Low density parity check (LDPC); Machine learning; Nonlinear channel; Soft decoding; Support vector machine (SVM)

Indexed keywords

CODING; EQUALIZATION; GAUSSIAN PROCESSES; LOW DENSITY PARITY CHECK; MACHINE-LEARNING; NONLINEAR CHANNEL; SOFT-DECODING; SUPPORT VECTOR;

BIT ERROR RATE; EQUALIZERS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC);

DECODING;

EID: 77955683748     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2009.2034941     Document Type: Article

References (42)

1. J. G. Proakis and D. G. Manolakis, Digital Communications. Englewood Cliffs, NJ: Prentice-Hall, 2007.
2. B. Mitchinson and R. F. Harrison, "Digital communications channel equalization using the kernel adaline," IEEE Trans. Commun., vol. 50, no. 4, pp. 571-576, 2002. (Pubitemid 34669788)
3. J. G. Proakis and M. Salehi, Communication Systems Engineering, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2002.
4. S. Chen, G. J. Gibson, C. F. N. Cowan, and P. M. Grant, "Adaptative equalization of finite non-linear channels using multilayer perceptrons," Signal Process., vol. 10, pp. 107-119, 1990. (Pubitemid 20718694)
5. S. Chen, G. J. Gibson, C. F. N. Cowan, and P. M. Grant, "Reconstruction of binary signals using an adaptive radial-basis-function equalizer," Signal Process., vol. 22, pp. 77-83, 1991.
6. J. Cid-Sueiro, A. Artés-Rodríguez, and A. R. Figueiras-Vidal, "Recurrent radial basis function networks for optimal symbol-by-symbol equalization," Signal Process., vol. 40, pp. 53-63, 1994.
7. P. R. Chang and B. C.Wang, "Adaptive decision feedback equalization for digital satellites channels using multilayer neural networks," IEEE J. Sel. Areas Commun., vol. 13, no. 2, pp. 316-324, Feb. 1995.
8. F. Pérez-Cruz, A. Navia-Vázquez, P. L. Alarcón- Diana, and A. Artés-Rodríguez, "SVC-based equalizer for burst TDMA transmissions," Signal Process., vol. 81, no. 8, pp. 1681-1693, Aug. 2001. (Pubitemid 32694055)
9. T. Kohonen, K. Raivio, O. Simula, O. Venta, and J. Henriksson, "Design of an SCRFNN-based nonlinear channel equaliser," in IEEE Proc. Commun., Banff, Canada, Dec. 2005, vol. 1552, pp. 771-779.
10. F. Pérez-Cruz, J. Murillo-Fuentes, and S. Caro, "Nonlinear channel equalization with Gaussian processes for regression," IEEE Trans. Signal Process., vol. 56, no. 10, pp. 5283-5286, Oct. 2008.
11. D. J. C. MacKay, "Good error-correcting codes based on very sparse matrices," IEEE Trans. Inf. Theory, vol. 45, no. 2, pp. 399-431, 1999.
12. T. Richardson and R. Urbanke, "Efficient encoding of low-density parity-check codes," IEEE Trans. Inf. Theory, vol. 72, no. 2, pp. 638-656, 2001. (Pubitemid 32318094)
13. H. Zhong and T. Zhang, "Block-LDPC: A practical LDPC coding system design approach," IEEE Trans. Circuits Syst. I, vol. 52, no. 4, 2005.
14. M. G. Luby,M.Mitzenmacher,M. A. Shokrollahi, D. A. Spielman, and V. Stemann, "Practical loss-resilient codes," in Proc. 29th Ann. ACM Symp. Theory of Computing, 1997, pp. 150-159.
15. S. Chung, D. Forney, T. Richardson, and R. Urbanke, "On the design of low-density parity-check codes within 0.0045 dB of the Shannon limit," IEEE Commun. Lett., vol. 5, no. 2, pp. 58-60, 2001. (Pubitemid 32286625)
16. D. Forney, "The Viterbi algorithm," IEEE Proc., vol. 61, no. 2, pp. 268-278, Mar. 1973.
17. L. R. Bahl, J. Cocke, F. Jelinek, and J. Raviv, "Optimal decoding of linear codes for minimizing symbol error rate," IEEE Trans. Inf. Theory, vol. 20, no. 2, pp. 284-287, Mar. 1974.
18. J. C. Platt, "Probabilities for SV machines," in Advances in Large Margin Classifiers, A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, Eds. Cambridge, MA: MIT Press, 2000, pp. 61-73.
19. J.Kwok, "Moderating the outputs of support vector machine classifier," IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 1018-1031, Sep. 1999.
20. D. MacKay, "The evidence framework applied to classification networks," Neural Computat., vol. 4, no. 5, pp. 720-736, 1992.
21. V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
22. C. K. I.Williams and C. E. Rasmussen, "Gaussian processes for regression," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 1996, vol. 8, pp. 598-604.
23. C. K. I.Williams and D. Barber, "Bayesian classification with Gaussian processes," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 12, pp. 1342-1351, Dec. 1998. (Pubitemid 128741378)
24. M. Kuss and C. Rasmussen, "Assessing approximate inference for binary Gaussian process classification," Machine Learn. Res., vol. 6, pp. 1679-1704, Oct. 2005.
25. F. Pérez-Cruz and J. J. Murillo-Fuentes, "Digital communication receivers using Gaussian processes for machine learning," EURASIP J. Adv. Signal Process., vol. 2008, 2008.
26. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. Cambridge, U.K.: MIT Press, 2006.
27. T. Minka, "Expectation propagation for approximate Bayesian inference," UAI, pp. 362-369, 2001.
28. F. Pérez-Cruz and O. Bousquet, "Kernel methods and their potential use in signal processing," Signal Process. Mag., vol. 21, no. 3, pp. 57-65, 2004.
29. D. J. C. MacKay, Information Theory, Inference and Learning Algorithms. Cambridge, U.K.: Cambridge Univ. Press, 2003.
30. H. Loeliger, "An introduction to factor graphs," IEEE Signal Process. Mag., pp. 28-41, Jan. 2004.
31. F. R. Kschischang, B. I. Frey, and H. A. Loeliger, "Factor graphs and the sum-product algorithm," IEEE Trans. Inf. Theory, vol. 47, no. 2, pp. 498-519, Feb. 2001. (Pubitemid 32318087)
32. Learning in Graphical Models, M. I. Jordan, Ed. Cambridge, MA: MIT Press, 1999.
33. E. Sudderth and W. Freeman, "Signal and image processing with belief propagation [DSP applications]," IEEE Signal Process. Mag., vol. 25, pp. 114-141, Mar. 2008.
34. T. Richardson and R. Urbanke, Modern Coding Theory. Cambridge, U.K.: Cambridge Univ. Press, 2008.
35. B. Schölkopf and A. Smola, LearningWithKernels. Cambridge, MA: MIT Press, 2001.
36. J. C. Platt, "Sequential minimal optimization: A fast algorithm for training support vector machines," in Advances in Kernel Methods-Support Vector Learning, B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds. Cambridge, MA: MIT Press, 1999, pp. 185-208.
37. E. Snelson and Z. Ghahramani, "Sparse Gaussian processes using pseudo-inputs," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2006, vol. 18, pp. 1257-1264.
38. L. Csató and M. Opper, "Sparse online Gaussian processes," Neural Computat., vol. 14, pp. 641-668, 2002.
39. C. M. Bishop, Neural Networks for Pattern Recognition. New York: Clarendon, 1995.
40. B. Majhi and G. Panda, "Recovery of digital information using bacterial foraging optimization based nonlinear channel equalizers," in Proc. IEEE 1st Int. Conf. Digit. Inf. Manage., Dec. 2006, pp. 367-372.
41. J. Patra, R. Pal, R. Baliarsingh, and G. Panda, "Nonlinear channel equalization for QAM signal constellation using artificial neural networks," IEEE Trans. Syst., Man, Cybern. Part B, vol. 29, pp. 262-271, Apr. 1999.
42. M. Mesiya, P. McLane, and L. Campbell, "Maximum likelihood sequence estimation of binary sequences transmitted over bandlimited nonlinear channels," IEEE Trans. Commun., vol. 25, pp. 633-643, Apr. 1977. (Pubitemid 8565129)