Blind iterative channel identification and equalization

We propose an iterative solution to the problem of blindly and jointly identifying the channel response and transmitted symbols in a digital communications system. The proposed algorithm iterates between a symbol estimator, which uses tentative channel estimates to provide soft symbol estimates, and a channel estimator, which uses the symbol estimates to improve the channel estimates. The proposed algorithm shares some similarities with the expectation-maximization (EM) algorithm but with lower complexity and better convergence properties. Specifically, the complexity of the proposed scheme is linear in the memory of the equalizer, and it avoids most of the local maxima that trap the EM algorithm.

[1]  M. Tsatsanis,et al.  Stochastic maximum likelihood methods for semi-blind channel estimation , 1998, IEEE Signal Processing Letters.

[2]  M. Tsatsanis,et al.  Stochastic maximum likelihood methods for semi-blind channel equalization , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[3]  Barruquer Moner.  IX. References , 1971 .

[4]  Paul G. Flikkema,et al.  Near-optimum iterative estimation of dispersive multipath channels , 1998, VTC '98. 48th IEEE Vehicular Technology Conference. Pathway to Global Wireless Revolution (Cat. No.98CH36151).

[5]  Ghassan Kawas Kaleh,et al.  Joint parameter estimation and symbol detection for linear or nonlinear unknown channels , 1994, IEEE Trans. Commun..

[6]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[7]  Alain Glavieux,et al.  Iterative correction of intersymbol interference: Turbo-equalization , 1995, Eur. Trans. Telecommun..

[8]  B.-P. Paris.  Self-adaptive maximum-likelihood sequence estimation , 1993, Proceedings of GLOBECOM '93. IEEE Global Telecommunications Conference.

[9]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[10]  Sylvie Perreau,et al.  Reduced computation blind equalization for FIR channel input Markov models , 1995, Proceedings IEEE International Conference on Communications ICC '95.

[11]  José A. R. Fonollosa,et al.  Blind channel estimation and data detection using hidden Markov models , 1997, IEEE Trans. Signal Process..

[12]  J.D. Villasenor,et al.  Blind turbo decoding and equalization , 1999, 1999 IEEE 49th Vehicular Technology Conference (Cat. No.99CH36363).

[13]  Behnaam Aazhang,et al.  Multistage detection in asynchronous code-division multiple-access communications , 1990, IEEE Trans. Commun..

[14]  J. A. Catipovic,et al.  Algorithms for joint channel estimation and data recovery-application to equalization in underwater communications , 1991 .

[15]  A. Glavieux,et al.  Near Shannon limit error-correcting coding and decoding: Turbo-codes. 1 , 1993, Proceedings of ICC '93 - IEEE International Conference on Communications.

[16]  John Cocke,et al.  Optimal decoding of linear codes for minimizing symbol error rate (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[17]  John B. Moore,et al.  On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure , 1993, IEEE Trans. Signal Process..

[18]  Chrysostomos L. Nikias,et al.  An ML/MMSE estimation approach to blind equalization , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[19]  Elisabeth de Carvalho,et al.  Blind and semi-blind maximum likelihood methods for FIR multichannel identification , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).