On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation

In this paper, the theory of hidden Markov models (HMM) is applied to the problem of blind (without training sequences) channel estimation and data detection. Within a HMM framework, the Baum–Welch(BW) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedure assumes the model (i.e., the channel response) to be static throughout the observation sequence. By means of introducing a parametric model for time-varying channel responses, a version of the algorithm, which is more appropriate for mobile channels [time-dependent Baum-Welch (TDBW)] is derived. Aiming to compare algorithm behavior, a set of computer simulations for a GSM scenario is provided. Results indicate that, in comparison to other Baum–Welch (BW) versions of the algorithm, the TDBW approach attains a remarkable enhancement in performance. For that purpose, only a moderate increase in computational complexity is needed.Peer Reviewe

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

Performance of MC-CDMA with pilot code and blind equalization algorithm

Various methods and techniques have been introduced and applied to advance the state of the art of mobile wireless communications technology. For example, some techniques are applied to overcome the problem of multipath caused by the mobile environment. Multipath produces replicas of the wanted signal which arrive at the receiver with different time delays. If not dealt with properly, this environment will greatly deteriorate the quality of the wanted signal. The so-called multiuser feature of many wireless communication systems will also add some interference to the signal of interest. This thesis makes an attempt to improve the performance of wireless communication systems that use either pilot-based or blind equalization techniques to obtain channel side information. Specifically, these techniques are concerned with the estimation of multipath parameters in order to improve system performance. By inserting some pre-defined code which is called a pilot code on each pre-defined segment of a data block, we can recover the signal by pilot-based methods. Using an adaptive method, some knowledge of the channel characteristics and input source, we can achieve acceptable error rate using blind equalization as an alternate solution to pilot-based. Finally, new enhancements are added to blind equalization to improve its performance further

Blind Channel Estimation and Data Detection Using Hidden Markov Models

In this correspondence, we propose applying the hidden Markov models (HMM) theory to the problem of blind channel estimation and data detection. The Baum--Welch (BW) algorithm, which is able to estimate all the parameters of the model, is enriched by introducing some linear constraints emerging from a linear FIR hypothesis on the channel. Additionally, a version of the algorithm that is suitable for timevarying channels is also presented. Performance is analyzed in a GSM environment using standard test channels and is found to be close to that obtained with a nonblind receiver

Blind channel estimation and data detection using hidden Markov models theory

In this correspondence, we propose applying the hidden Markov models (HMM) theory to the problem of blind channel estimation and data detection. The Baum–Welch (BW) algorithm, which is able to estimate all the parameters of the model, is enriched by introducing some linear constraints emerging from a linear FIR hypothesis on the channel. Additionally, a version of the algorithm that is suitable for timevarying channels is also presented. Performance is analyzed in a GSM environment using standard test channels and is found to be close to that obtained with a nonblind receiver.Peer Reviewe