Robust equalization of multichannel acoustic systems

In most real-world acoustical scenarios, speech signals captured by distant microphones from a source are reverberated due to multipath propagation, and the reverberation may impair speech intelligibility. Speech dereverberation can be achieved by equalizing the channels from the source to microphones. Equalization systems can be computed using estimates of multichannel acoustic impulse responses. However, the estimates obtained from system identification always include errors; the fact that an equalization system is able to equalize the estimated multichannel acoustic system does not mean that it is able to equalize the true system. The objective of this thesis is to propose and investigate robust equalization methods for multichannel acoustic systems in the presence of system identification errors. Equalization systems can be computed using the multiple-input/output inverse theorem or multichannel least-squares method. However, equalization systems obtained from these methods are very sensitive to system identification errors. A study of the multichannel least-squares method with respect to two classes of characteristic channel zeros is conducted. Accordingly, a relaxed multichannel least- squares method is proposed. Channel shortening in connection with the multiple- input/output inverse theorem and the relaxed multichannel least-squares method is discussed. Two algorithms taking into account the system identification errors are developed. Firstly, an optimally-stopped weighted conjugate gradient algorithm is proposed. A conjugate gradient iterative method is employed to compute the equalization system. The iteration process is stopped optimally with respect to system identification errors. Secondly, a system-identification-error-robust equalization method exploring the use of error models is presented, which incorporates system identification error models in the weighted multichannel least-squares formulation

Low Complexity Blind Equalization for OFDM Systems with General Constellations

This paper proposes a low-complexity algorithm for blind equalization of data in OFDM-based wireless systems with general constellations. The proposed algorithm is able to recover data even when the channel changes on a symbol-by-symbol basis, making it suitable for fast fading channels. The proposed algorithm does not require any statistical information of the channel and thus does not suffer from latency normally associated with blind methods. We also demonstrate how to reduce the complexity of the algorithm, which becomes especially low at high SNR. Specifically, we show that in the high SNR regime, the number of operations is of the order O(LN), where L is the cyclic prefix length and N is the total number of subcarriers. Simulation results confirm the favorable performance of our algorithm

Blind joint maximum likelihood channel estimation and data detection for single-input multiple-output systems

A blind adaptive scheme is proposed for joint maximum likelihood (ML) channel estimation and data detection of single-input multiple-output (SIMO) systems. The joint ML optimization of the channel and data estimation is decomposed into an iterative optimization loop. An efficient global optimization algorithm termed as the repeated weighted boosting aided search is employed first to identify the unknown SIMO channel model, and then the Viterbi algorithm is used for the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used for demonstrating the efficiency of this joint ML optimization scheme designed for blind adaptive SIMO systems

The Extended-window Channel Estimator For Iterative Channel-and-symbol Estimation

The application of the expectation-maximization (EM) algorithm to channel estimation results in a well-known iterative channel-and-symbol estimator (ICSE). The EM-ICSE iterates between a symbol estimator based on the forward-backward recursion (BCJR equalizer) and a channel estimator, and may provide approximate maximum-likelihood blind or semiblind channel estimates. Nevertheless, the EM-ICSE has high complexity, and it is prone to misconvergence. In this paper, we propose the extended-window (EW) estimator, a novel channel estimator for ICSE that can be used with any soft-output symbol estimator. Therefore, the symbol estimator may be chosen according to performance or complexity specifications. We show that the EW-ICSE, an ICSE that uses the EW estimator and the BCJR equalizer, is less complex and less susceptible to misconvergence than the EM-ICSE. Simulation results reveal that the EW-ICSE may converge faster than the EM-ICSE. © 2005 Hindawi Publishing Corporation.200529299Barry, J.R., Lee, E.A., Messerschmitt, D.G., (2003) Digital Communications, , Kluwer Academic Publishers, Norwell, Mass, USA, 3rd editionAyadi, J., De Carvalho, E., Slock, D.T.M., Blind and semi-blind maximum likelihood methods for FIR multichannel identification (1998) Proc. IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP'98), 6, pp. 3185-3188. , Seattle, Wash, USA, MayFeder, M., Catipovic, J.A., Algorithms for joint channel estimation and data recovery-application to equalization in underwater communications (1991) IEEE J. Oceanic Eng., 16 (1), pp. 42-55Kaleh, G.K., Vallet, R., Joint parameter estimation and symbol detection for linear or nonlinear unknown channels (1994) IEEE Trans. Commun., 42 (7), pp. 2406-2413Anton-Haro, C., Fonollosa, J.A.R., Fonollosa, J.R., Blind channel estimation and data detection using hidden Markov models (1997) IEEE Trans. Signal Processing, 45 (1), pp. 241-247Garcia-Frias, J., Villasenor, J.D., Combined turbo detection and decoding for unknown ISI channels (2003) IEEE Trans. Commun., 51 (1), pp. 79-85Kammeyer, K.-D., Kühn, V., Petermann, T., Blind and nonblind turbo estimation for fast fading GSM channels (2001) IEEE J. Select. Areas Commun., 19 (9), pp. 1718-1728Berthet, A.O., Ünal, B.S., Visoz, R., Iterative decoding of convolutionally encoded signals over multipath Rayleigh fading channels (2001) IEEE J. Select. Areas Commun., 19 (9), pp. 1729-1743Lopes, R.R., Barry, J.R., Exploiting error-control coding in blind channel estimation (2001) IEEE Global Communications Conference (GLOBECOM'01), 2, pp. 1317-1321. , San Antonio, Tex, USA, NovemberKrishnamurthy, V., Moore, J.B., On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure (1993) IEEE Trans. Signal Processing, 41 (8), pp. 2557-2573White, L.B., Perreau, S., Duhamel, P., Reduced computation blind equalization for FIR channel input Markov models (1995) IEEE International Conference on Communications (ICC'95), 2, pp. 993-997. , Seattle, Wash, USA, JuneShao, M., Nikias, C.L., An ML/MMSE estimation approach to blind equalization (1994) Proc. IEEE International Conference on Acoustics, Speech, Signal Processing (ICASSP'94), 4, pp. 569-572. , Adelaide, SA, Australia, AprilCirpan, H.A., Tsatsanis, M.K., Stochastic maximum likelihood methods for semi-blind channel estimation (1998) IEEE Signal Processing Lett., 5 (1), pp. 21-24Paris, B.-P., Self-adaptive maximum-likelihood sequence estimation (1993) IEEE Global Communications Conference (GLOBECOM'93), 4, pp. 92-96. , Houston, Tex, USA, November-DecemberBaum, L.E., Petrie, T., Soules, G., Weiss, N., A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains (1970) Annals of Mathematics Statistics, 41 (1), pp. 164-171Dempster, A.P., Laird, N.M., Rubin, D.B., Maximum likelihood from incomplete data via the em algorithm (1977) Journal of the Royal Statistics Society, 39 (1), pp. 1-38Bahl, L.R., Cocke, J., Jelinek, F., Raviv, J., Optimal decoding of linear codes for minimizing symbol error rate (1974) IEEE Trans. Inform. Theory, 20 (2), pp. 284-287Berrou, C., Glavieux, A., Thitimajshima, P., Near Shannon limit error-correcting coding and decoding: Turbo-codes (1993) IEEE International Conference on Communications (ICC'93), 2, pp. 1064-1070. , Geneva, Switzerland, MayBenedetto, S., Divsalar, D., Montorsi, G., Pollara, F., Serial concatenation of interleaved codes: Performance analysis, design, and iterative decoding (1998) IEEE Trans. Inform. Theory, 44 (3), pp. 909-926Tüchler, M., Koetter, R., Singer, A.C., Turbo equalization: Principles and new results (2002) IEEE Trans. Commun., 50 (5), pp. 754-767Lopes, R.R., Barry, J.R., Soft-output decision-feedback equalization with a priori information (2003) IEEE Global Communications Conference (GLOBECOM'03), 3, pp. 1705-1709. , San Francisco, Calif, USA, DecemberPoor, H.V., (1994) An Introduction to Signal Detection and Estimation, , Springer-Verlag, New York, NY, USA, 2nd editionMontemayor, C.A., Flikkema, P.G., Near-optimum iterative estimation of dispersive multipath channels (1998) IEEE 48th Vehicular Technology Conference (VTC'98), 3, pp. 2246-2250. , Ottawa, ON, Canada, MaySandell, M., Luschi, C., Strauch, P., Yan, R., Iterative channel estimation using soft decision feedback (1998) IEEE Global Communications Conference (GLOBECOM'98), 6, pp. 3728-3733. , Sydney, NSW, Australia, Novembe