9,662 research outputs found
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
Non-parametric Estimation of Mutual Information with Application to Nonlinear Optical Fibers
This paper compares and evaluates a set of non-parametric mutual information
estimators with the goal of providing a novel toolset to progress in the
analysis of the capacity of the nonlinear optical channel, which is currently
an open problem. In the first part of the paper, the methods of the study are
presented. The second part details their application to several
optically-related channels to highlight their features.Comment: This work has been submited to IEEE International Symposium on
Information Theor
Nonlinearity Mitigation in WDM Systems: Models, Strategies, and Achievable Rates
After reviewing models and mitigation strategies for interchannel nonlinear
interference (NLI), we focus on the frequency-resolved logarithmic perturbation
model to study the coherence properties of NLI. Based on this study, we devise
an NLI mitigation strategy which exploits the synergic effect of phase and
polarization noise compensation (PPN) and subcarrier multiplexing with
symbol-rate optimization. This synergy persists even for high-order modulation
alphabets and Gaussian symbols. A particle method for the computation of the
resulting achievable information rate and spectral efficiency (SE) is presented
and employed to lower-bound the channel capacity. The dependence of the SE on
the link length, amplifier spacing, and presence or absence of inline
dispersion compensation is studied. Single-polarization and dual-polarization
scenarios with either independent or joint processing of the two polarizations
are considered. Numerical results show that, in links with ideal distributed
amplification, an SE gain of about 1 bit/s/Hz/polarization can be obtained (or,
in alternative, the system reach can be doubled at a given SE) with respect to
single-carrier systems without PPN mitigation. The gain is lower with lumped
amplification, increases with the number of spans, decreases with the span
length, and is further reduced by in-line dispersion compensation. For
instance, considering a dispersion-unmanaged link with lumped amplification and
an amplifier spacing of 60 km, the SE after 80 spans can be be increased from
4.5 to 4.8 bit/s/Hz/polarization, or the reach raised up to 100 spans (+25%)
for a fixed SE.Comment: Submitted to Journal of Lightwave Technolog
Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference
This paper presents a general stochastic model developed for a class of
cooperative wireless relay networks, in which imperfect knowledge of the
channel state information at the destination node is assumed. The framework
incorporates multiple relay nodes operating under general known non-linear
processing functions. When a non-linear relay function is considered, the
likelihood function is generally intractable resulting in the maximum
likelihood and the maximum a posteriori detectors not admitting closed form
solutions. We illustrate our methodology to overcome this intractability under
the example of a popular optimal non-linear relay function choice and
demonstrate how our algorithms are capable of solving the previously
intractable detection problem. Overcoming this intractability involves
development of specialised Bayesian models. We develop three novel algorithms
to perform detection for this Bayesian model, these include a Markov chain
Monte Carlo Approximate Bayesian Computation (MCMC-ABC) approach; an Auxiliary
Variable MCMC (MCMC-AV) approach; and a Suboptimal Exhaustive Search Zero
Forcing (SES-ZF) approach. Finally, numerical examples comparing the symbol
error rate (SER) performance versus signal to noise ratio (SNR) of the three
detection algorithms are studied in simulated examples
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