QAM constellation classification based on statistical sampling for linear distortive channels

IEEE Transactions on Signal Processing

Volumn 54, Issue 5, 2006, Pages 1575-1586

  Drumright, Thomas A ^a   Ding, Zhi ^b  

^a NOKIA RESEARCH CENTER   (United States)

^b University of California   (United States)

Author keywords

Channel estimation; Constellation classification; Gibbs sampling; Monte Carlo sampling; QAM classification

Indexed keywords

COMMUNICATION CHANNELS (INFORMATION THEORY); ESTIMATION; FADING (RADIO); MONTE CARLO METHODS; SAMPLING; STATISTICAL METHODS;

CHANNEL ESTIMATION; GIBBS SAMPLING; LINEAR DISTORTIVE CHANNELS;

QUADRATURE AMPLITUDE MODULATION;

EID: 33646018893     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2006.871972     Document Type: Article

References (28)

1. C. Long, K. Chugg, and A. Polydoros, "Further results in maximum likelihood classification of QAM signals," in Proc. IEEE Military Communications Conf. (MILCOM), Long Branch, NJ, 1994, pp. 57-61.
2. W. Wei and J. M. Mendel, "A new maximum-likelihood method for modulation classification," in Proc. Asilomar Conf., 1996, pp. 1132-1135.
3. N. Benvenuto and T. W. Goeddel, "Classification of voiceband data signals using the constellation magnitude," IEEE Trans. Commun., vol. 43, no. 11, pp. 2759-2770, Nov. 1995.
4. S. L. Wood, M. G. Larimore, and J. R. Treichler, "Modem constellation identification: A performance comparison of two methods," in Proc. Int. Conf. Acoustics, Signal Processing (ICASSP), vol. 3, Albuquerque, NM, 1990, pp. 1651-1654.
5. T. Drumright and Z. Ding, "A new algorithm for QAM classification AWGN channels," in Proc. Int. Symp. Circuits Systems (ISCAS), Phoenix, AZ, 2002, pp. 849-852.
6. A. Swami and B. M. Sadler, "Hierarchical digital modulation classification using cumulants," IEEE Trans. Commun., vol. 48, no. 3, pp. 416-429, Mar. 2000.
7. Q. Zhu, M. Kam, and R. Yeager, "Non-parametric identification of QAM constellations in noise," in Proc. Int. Conf. Acoustics, Signal Processing (ICASSP), vol. 4, Minneapolis, MN, 1993, pp. 184-187.
8. A. O. Hero and H. Hadinejad-Mahram, "Digital modulation classification using power moment matrices," in Proc. Int. Conf. Acoustics, Signal Processing (ICASSP), Seattle, WA, 1998, pp. 3285-3288.
9. H. Roufarshbaf and H. Amindavar, "High-speed voiceband QAM constellation classification in multipath environment," in Proc. 2002 12th IEEE Workshop Neural Networks Signal Processing, 2002, pp. 455-464.
10. N. Lay and A. Polydoros, "Modulation classification of signals in unknown ISI environments," in Proc. IEEE Military Communications Conf. (MILCOM), 1995, pp. 170-174.
11. R. Chen and T. Li, "Blind restoration of linearly degraded discrete signals by Gibbs sampling," IEEE Trans. Signal Process., vol. 43, no. 10, pp. 2410-2414, Oct. 1995.
12. T. Drumright and Z. Ding, "Gibbs classification of QAM constellations under frequency selective fading," in Proc. 36th Asilomar Conf., Asilomar, CA, 2002, pp. 833-837.
13. A. E. Gelfand and A. P. M. Smith, "Sampling-based approaches to calculating marginal densities," J. Amer. Statist. Assn., vol. 85, pp. 389-409, Jun. 1990.
14. A. F. M. Smith and G. O. Roberts, "Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods," J. R. Statist. Soc., ser. B, vol. 55, pp. 3-23, 1993.
15. S. L. Zeger and M. Rezaul Karim, "Generalized linear models with random effects; a Gibbs sampling approach," J. Amer. Statist. Assn., vol. 86, pp. 79-86, Mar. 1991.
16. R. Chen, J. S. Liu, and X. Wang, "Convergence analyzes and comparisons of Markov chain Monte Carlo algorithms in digital communications," IEEE Trans. Signal Process., vol. 50, no. 2, pp. 255-270, Feb. 2002.
17. T. Clapp and S. Godsill, "Bayesian blind deconvolution for mobile communications," in Inst. Elect. Eng. Colloquium Adaptive Signal Processing Mobile Communication Systems, 1997, Ref. no. 1997/383, pp. 9/ 1-9/6.
18. S. Lesage, J.-Y. Tourneret, and P. M. Djuric, "Classification of digital modulations by MCMC sampling," in Proc. Int. Conf. Acoustics, Signal Processing (ICASSP), 2001, pp. 2553-2556.
19. J. M. Mendel, Lessons in Estimation Theory for Signal Processing, Communications, and Control. Englewood Cliffs, NJ: Prentice-Hall, 1995.
20. S. Richardson and P. J. Green, "On bayesian analysis of mixtures with an unknown number of components," J. R. Statist. Soc., ser. B, vol. 59, pp. 731-792, 1997.
21. E. Punskaya, C. Andrieu, and A. Doucet, "Bayesian curve fitting using MCMC with applications to signal segmentation," IEEE Trans. Signal Process., vol. 50, no. 3, pp. 747-758, Mar. 2002.
22. G. Casella and R. L. Berger, Statistical Inference, 1st ed. Belmont, CA: Duxbury Press, 1990.
23. A. Gelman, J. B. Carlin, H. S. Stearn, and D. B. Rubin, Bayesian Data Analysis. London, U.K.: Chapman & Hall, 1995.
24. W. H. Gerstacker and D. P. Taylor, "IEEE signal processing letters," J. Amer. Statist. Assn., vol. 10, pp. 39-42, Feb. 2003.
25. A. P. Liavas, P. A. Regalia, and J.-P. Delmas, "Blind channel approximation: Effective channel order determination," IEEE Trans. Signal Process., vol. 47, no. 12, pp. 3336-3344, Dec. 1999.
26. H. Saarnisaari, "Estimation of numbers of multipaths in DS-CDMA by the MDL principle," in Proc. 5th Int. Symp. Spread Spectrum Techniques Applications, vol. 1, Sep. 1998, pp. 258-261.
27. J. G. Proakis, Digital Communications, 3rd ed. New York: McGraw-Hill, 1995.
28. Z. Ding and Y. Li, Blind Equalization and Identification. New York: Marcel Dekker, 2001.