Actas de congresos
Genetic Algorithms For Blind Maximum-likelihood Receivers
Registro en:
780386086
Machine Learning For Signal Processing Xiv - Proceedings Of The 2004 Ieee Signal Processing Society Workshop. , v. , n. , p. 685 - 694, 2004.
2-s2.0-17644418452
Autor
De F. Attux R.R.
Lopes R.R.
De Castro L.N.
Von Zuben F.J.
Romano J.M.T.
Institución
Resumen
The ultimate receiver in a communications system is one that minimizes the bit-error rate (BER) or, equivalently, that maximizes the likelihood function. Unfortunately, a maximum-likelihood (ML) receiver can be prohibitively complex in some cases. For instance, in a blind system, where neither the channel nor any part of the transmitted sequence are known, an ML receiver would have to test all possible transmitted sequences to determine the one that minimizes the BER. In this paper, we derive a likelihood function for blind communications, and we use a genetic algorithm as the optimization strategy, at a reasonable computational cost. The performance of the resulting algorithm can be improved by exploiting structural aspects of the transmitted sequence that are normally neglected by blind techniques, such as the presence of some known symbols or of an error-control code. Simulation results are presented to validate the proposal. © 2004 IEEE.
685 694 Barry, J.R., Lee, E.A., Messerschmitt, D.G., (2003) Digital Communication, Third Edition, , Kluwer Academic Publishers Ayadi, J., De Carvalho, E., Slock, D.T.M., Blind and semi-blind maximum-likelihood methods for FIR multichannel identification (1998) IEEE ICASSP, 6, pp. 3185-3188 Haykin, S., (1994) Blind Deconvolution, , Prentice-Hall Davis, L., (1991) Handbook of Genetic Algorithms, , Van Nostrand Reinhold Chen, S., Wu, Y., Maximum likelihood joint channel and data estimation using genetic algorithms (1998) IEEE Trans. Signal Proc., 46 (5), pp. 1469-1473. , May Horn, R.A., Johnson, C.R., (1985) Matrix Analysis, , Cambridge University Press Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via the EM algorithm (1997) Journal of the Royal Statistics Society, 39 (1), pp. 1-38