Window-based expectation propagation for adaptive signal detection in flat-fading channels

IEEE Transactions on Wireless Communications

Volumn 6, Issue 1, 2007, Pages 348-355

  Qi, Yuan ^a   Minka, Thomas P ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b MICROSOFT RESEARCH   (United Kingdom)

Author keywords

Bayesian inference; Expectation propagation; Fading channels; Monte Carlo methods; Nonlinear or non Gaussian systems; Sequential estimation; Signal detection

Indexed keywords

COMPUTATIONAL COMPLEXITY; ELECTROMAGNETIC WAVE PROPAGATION; FADING CHANNELS; GRAPHIC METHODS; MONTE CARLO METHODS; SIGNAL DETECTION;

BAYESIAN INFERENCE; BAYESIAN RECEIVERS; DISCRETE VARIABLES; HYBRID DYNAMIC SYSTEMS; SEQUENTIAL ESTIMATION;

SIGNAL RECEIVERS;

EID: 33947589644     PISSN: 15361276     EISSN: None     Source Type: Journal    
DOI: 10.1109/TWC.2007.05237     Document Type: Article

References (22)

1. G. M. Vitetta and D. P. Taylor, "Maximum likelihood decoding of uncoded and coded PSK signal sequences transmitted over Rayleigh flat-fading channels," IEEE Trans. Commun., vol. 43, pp. 2750-2758, 1995.
2. A. N. D'Andrea, A. Diglio, and U. Mengali, "Symbol-aided channel estimation with nonselective Rayleigh fading channels," IEEE Trans. Veh. Technol., vol. 44, pp. 41-48, Feb. 1995.
3. P. Hoeher and J. H. Lodge, "Turbo DPSK: iterative differential PSK demodulation and channel decodings," IEEE Trans. Commun., vol. 47, pp. 837-843, June 1999.
4. A. W. Eckford, "Channel estimation in block fading channels using the factor graph EM algorithm," in Proc. 22nd Biennial Symposium on Communications 2004.
5. R. Otnes and M. Tüchler, "Iterative channel estimation for turbo equalization of time-varying frequency-selective channels," IEEE Trans. Wireless Commun., vol. 3, no. 6, pp. 1918-1923, 2004.
6. R. Chen, X. Wang, and J. S. Liu, "Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering," IEEE Trans. Inf. Theory, vol. 46, no. 6, pp. 2079-2094, Sept. 2000.
7. X. Wang, R. Chen, and D. Guo, "Delayed-pilot sampling for mixture Kalman filter with application in fading channels," IEEE Trans. Signal Processing, special issue on Monte Carlo methods for statistical signal processing, vol. 50, no. 2, pp. 241-254, Feb. 2002.
8. A. P. Worthen and W. E. Stark, "Unified design of iterative receivers using factor graphs," IEEE Trans. Inf. Theory, vol. 47, no. 2, pp. 843-849, 2001.
9. G. Colavolpe, A. Barbieri, and G. Caire, "Algorithms for iterative decoding in the presence of strong phase noise," IEEE J. Sel. Areas Commun., vol. 23, no. 9, pp. 1748-1757, 2005.
10. T. P. Minka, "Expectation propagation for approximate Bayesian inference," in Uncertainty in Artificial Intelligence, 2001.
11. H. J. Kushner and A. S. Budhiraja, "A nonlinear filtering algorithm based on an approximation of the conditional distribution," IEEE Trans. Autom. Control, vol. 45, pp. 580-585, 2000.
12. X. Boyen and D. Koller, "Tractable inference for complex stochastic processes," in Uncertainty in Artificial Intelligence, 1998, vol. 11.
13. B. J. Frey, Graphical Models for Machine Learning and Digital Communication. Cambridge, MA: MIT Press, 1998.
14. R. H. Shumway and D. S. Stoffer, "Dynamic linear models with switching," J. The American Statistical Association, vol. 86, pp. 763-769, 1991.
15. C. Cargnoni, P. Mueller, and M. West, "Bayesian forecasting of multinomial time series through conditionally Gaussian dynamic models," J. American Statistical Association, vol. 92, pp. 587-606, 1997.
16. H. Tanizaki, "Nonlinear and non-Gaussian state-space modeling with Monte Carlo techniques: a survey and comparative study," in Handbook of Statistics: Stochastic Processes: Modeling and Simulation, C. R. Rao and D. N. Shanbhag, Eds., chapter 22. North-Holland, 2000.
17. A. Doucet, N. de Freitas, and N. Gordon, Eds., Sequential Monte Carlo Methods in Practice. Springer-Verlag, 2001.
18. W. Fong, S. Godsill, A. Doucet, and M. West, "Monte Carlo smoothing with application to audio signal enhancement," IEEE Trans. Signal Processing, vol. 50, no. 2, Feb 2003.
19. G. Welch and G. Bishop, "An introduction to the Kalman filter," Tech. Report, Department of Computer Science and Engineering, University of North Carolina at Chapel Hill, March 2002.
20. T. Heskes and O. Zoeter, "Expectation propagation for approximate inference in dynamic Bayesian networks," in Proc UAI'02.
21. E. A. Wan, R. van der Merwe, and A. T. Nelson, "Dual estimation and the unscented transformation," in NIPS 12, 2000, pp. 666-672.
22. T. P. Minka and Y. Qi, "Tree-structured approximations by expectation propagation," in NIPS, 2003.