Gaussian process regressors for multiuser detection in DS-CDMA systems

IEEE Transactions on Communications

Volumn 57, Issue 8, 2009, Pages 2339-2347

  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

Gaussian process for regression; Kernel methods; Multiuser detection; Nonlinear regression

Indexed keywords

CDMA COMMUNICATION SYSTEMS; CDMA SYSTEM; CROSS VALIDATION; DECISION FUNCTIONS; DIGITAL COMMUNICATIONS; DS-CDMA SYSTEM; GAUSSIAN PROCESS FOR REGRESSION; GAUSSIAN PROCESSES; KERNEL METHODS; LINEAR MINIMUM MEAN SQUARE ERRORS; MMSE DETECTORS; NEW APPROACHES; NON-LINEAR ESTIMATION; NON-LINEARITY; NONLINEAR ESTIMATOR; NONLINEAR EXTENSION; NONLINEAR MULTIUSER DETECTORS; NONLINEAR REGRESSION; OPTIMIZATION PROBLEMS; REGULARIZATION PARAMETERS; SHORT CODES;

CODE DIVISION MULTIPLE ACCESS; COMMUNICATION SYSTEMS; DIGITAL COMMUNICATION SYSTEMS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); MAXIMUM LIKELIHOOD ESTIMATION; MULTIUSER DETECTION; RADAR ANTENNAS; REGRESSION ANALYSIS; SIGNAL RECEIVERS;

DETECTORS;

EID: 69749101080     PISSN: 00906778     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCOMM.2009.08.070450     Document Type: Article

References (49)

1. S. Verdú, Multiuser Detection. Cambridge University Press, 1998.
2. R. Prasad, CDMA for Wireless Personal Communications. Norwood, MA: Artech House, 1996.
3. M. Zeng, A. Annamalai, and V K. Bhargava, "Recent advances in cellular wireless communications," IEEE Commun. Mag., vol. 37, no. 1, pp. 128-138, Sept. 1999.
4. J. G. Proakis, Digital Communications, 4th ed. New York: McGraw-Hill, 2000.
5. S. Verdd, "Minimum probability of error for asynchronous gaussian multiple access channels," IEEE Trans. Inform. Theory, vol. 32, pp. 85-96, Jan. 1986.
6. R. Lupas and S. Verdu, "Linear multiuser detectors for synchronous code-division multiple-access channels," IEEE Trans. Inform. Theory vol. 35, no. 1, pp. 123-136, Jan. 1989.
7. Z. Xie, R. Short, and C. Rushforth, "A family of suboptimum detectors for coherent multiuser communications," IEEE J. Select. Areas Commun., vol. 8, pp. 683-690, 1990.
8. U. Madhow and M. L. Honig, "MMSE interference suppression for direct-sequence spread-spectrum CDMA," IEEE Trans. Commun., vol. 42, pp. 3178-3188, 1994.
9. H. V Poor and S. Verdn, "Probability of error in MMSE multiuser detection," IEEE Trans. Inform. Theory, vol. 43, pp. 858-871, 1997.
10. A. N. Tikhonov and V Y Arsenin, Solution of III-Posed Problems. Wiston, Washington DC, 1977.
11. U. Madbow, "Blind adaptive interference suppression for direct sequence CDMA," Proc. IEEE, vol. 86, pp. 2048-2069, Oct. 1998.
12. J. W. Shubao Liu, "Blind adaptive multiuser detection using a recurrent neural netowrk," in Proc. IEEE Int. Conf. Commun., Circuits Systems (ICCCAS), vol. 2, June 2004, pp. 1071-1075.
13. K. Zarifi, S. Shahbazpanabi, A. B. Gershman, and Z.-Q. Luo, "Robust blind multiuser detection based on the worst-case performance optimization of the nimse receiver," IEEE Trans. Signal Processing vol. 57, no. 1, pp. 295-296, Jan. 2005.
14. C. M. Bishop, Neural Networks for Pattern Recognition. Clarendon Press, 1995.
15. L. Rugini, P. Banelli, and S. Cacopardi, "A full rank regularization technique for MMSE detection in multiuser CDMA systems," IEEE Commun. Lett., vol. 9, no. 1, p. 200, May 2005.
16. G. Wahba and S. Wold, "A completely automatic french curve: Fitting spline functions by cross-validation," Commun. Statistics, vol. Series A 4, no. 1, pp. 257-263, 1975.
17. M. L. Jordan, Ed., Learning in Graphical Models. Cambridge, MA: MIT Press, 1999.
18. K.-S. C. Anders Høst-Madsen, "MMSE/PIC multiuser detection for DS/CDMA systems with inter- and intra-cell interference," IEEE Trans. Commun., vol. 47, no. 2, p. 1999, Feb. 1999.
19. Y. Kabashima, "A CDMA multiuser detection algorithm on the basis of belief propagation," J. Physics A: Mathematical and General, vol. 36, no. 2003, pp. 11 111-11 121, Oct. 2003.
20. M. O. T. Tanaka, "Approximate belief propagation, density evolution, and statistical neurodynamics for CDMA multiuser detection," IEEE Trans. Inform. Theory, vol. 51, no. 2, pp. 700-706, Feb. 2005.
21. L. K. R. P. H. Tan, "Asymptotically optimal non-linear MMSE multiuser detection based on multivariate gaussian approximation," IEEE Trans. Commun., vol. 54, no. 8, pp. 1427-1438, Aug. 2006.
22. D. D. Lin and T. J. Lim, "A variational free energy minimization interpretation of multiuser detection in CDMA," in IEEE Global TeleCommun. Conf. (Globecom), vol. 3, St. Louis, MO, USA, Dec. 2005.
23. V. N. Vapnik, Statistical Learning Theory. New York: John Wiley & Sons, 1998.
24. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. Cambridge, MA: MIT Press, 2006.
25. A. J. Smola and B. Schölkopf, "A tutorial on support vector regression," Royal Holloway College, University of London, UK, Tech. Rep. NC-TR-98-030, 1998, [online]. Available: Ftp://www.neurocolt.com/ pub/neurocolt/tech_reports/1998/98030.ps.Z.
26. U. Mitra and H. V. Poor, "Neural network techniques for adaptive multiuser demodulation," IEEE J. Select. Areas Commun., vol. 12, pp. 1460-1470, 1994.
27. R. Tanner and D. G. M. Cruickshank, "Volterra based receivers for DS-CDMA," in Proc. IEEE Int. Symp. Personal, Indoor, Mobile Radio Commun., vol. 3, Sept. 1997, pp. 1166-1170.
28. S. Chen, A. K. Samingan, and L. Hanzo, "Support vector machine multiuser receiver for DS-CDMA signals in multipath channels," IEEE Trans. Neural Network, vol. 12, no. 3, pp. 604-611, Dec. 2001.
29. B. Aazbang, B. P. Paris, and G. C. Orsak, "Neural networks for multiuser detection in code-division multiple-access communications," IEEE Trans. Commun., vol. 40, pp. 1212-1222, 1992.
30. D. G. M. Cruickshank, "Radial basis function receivers for DS-CDMA," IEE Electron. Lett., vol. 32, no. 3, pp. 188-190, 1996.
31. B. Schölkopf and A. Smola, Learning with Kernels. M.I.T. Press, 2001.
32. G. S. Kimeldorf and G. Wahba, "Some results in Tchebycheffian spline functions," J. Mathematical Analysis Applications, vol. 33, pp. 82-95, 1971.
33. C. K. I. Williams and C. E. Rasmussen, "Gaussian processes for regression," in Proc. Conf. Advances in Neural Information Processing Systems, NIPS, D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, eds., vol. 8. MIT Press, 1995.
34. C. Williams, "Prediction with gaussian processes: From linear regression to linear prediction and beyond," in Learning and Inference in Graphical Models, M. Jordan, ed. Kluwer Academic Press, 1998.
35. J. Murillo-Fuentes, S. Caro, and E Perez-Cruz, "Gaussian processes for multiuser detection in CDMA receivers," in Advances in Neural Information Processing Systems, NIPS, Vancouver, CA, Dec. 2005.
36. F. Pérez-Cruz and J. Murillo-Fuentes, "Gaussian processes for digital communications," in Proc. ICASSP, vol. V, Tolousse, France, May 2006, pp. 781-784.
37. T. S. Rappaport, Wireless Communications. Prentice Hall PTR, 2002.
38. J. G. Proakis, "Adaptive equalization for TDMA digital mobile radio," IEEE Trans. Veh. Technol., vol. 40, no. 2, pp. 333-341, Feb. 1991.
39. C. K. I. Williams and D. Barber, "Bayesian classification with gaussian processes," IEEE Trans. Pattern Analysis Machine Intelligence, vol. 20, no. 12, pp. 1342-1351, 1998.
40. M. Kuss and C. E. Rasmussen, "Assessing approximations for Gaussian process classification," in Proc. Conf. Advances Neural Information Processing Systems, NIPS, Y. Weiss, B. Schölkopf, and J. Platt, Eds., vol. 18. MIT Press, 2006, pp. 699-706.
41. F. Pérez-Cruz and O. Bousquet, "Kernel methods and their potential use in signal processing," Signal Processing Mag., vol. 21, no. 3, pp. 57-65, 2004.
42. D. Guo, S. Shamai, and S. Verdú, "Mutual information and minimum mean-square error in gaussian channels," IEEE Trans. Inform. Theory vol. 51, no. 4, pp. 1261-1283, Apr. 2005.
43. A. J. Smola and P. L. Bartlett, "Sparse greedy gaussian process regression," in Advances Neural Information Processing Systems 13 T. K. Leen, T. G. Dietterich, and V. Tresp, eds. Cambridge, MA: MIT Press, 2001.
44. L. Csat'o and M. Opper, "Sparse online gaussian processes," Neural Computation, vol. 14, no. 3, pp. 641-669, 2002.
45. M. Seeger, C. K. I. Williams, and N. Lawrence, "Fast forward selection to speed up sparse gaussian process regression," in Proc. International Workshop Artificial Intelligence Statistics, C. Bishop and B. J. Frey, eds., 2003.
46. E. Snelson and Z. Ghahramani, "Sparse gaussian processes using pseudo-inputs," in Advances in Neural Information Processing Systems 18, Y. Weiss, B. Schölkopf, and J. Platt, Eds. Cambridge, MA: MIT Press, 2006.
47. E. Snelson and Z. G. (2007), "Local and global sparse gaussian process approximations," in Artificial Intelligence Statistics, vol. 11, 2007.
48. J. Quiñonero-Candela and C. E. Rasmussen, "A unifying view of sparse approximate gaussian process regression," J. Machine Learning Research, vol. 6, pp. 1935-1959, 2005.
49. L. Rugini, P. Banelli, and S. Cacopardi, "Regularized MMSE multiuser detection using covariance matrix tapering," in Proc. EEE International Conf. Commun., vol. 4, May 2003, pp. 2460-2464.