2,236 research outputs found
Error propagation and recovery in decision-feedback equalizers for nonlinear channels
©2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Nonlinear intersymbol interference is often present in communication and digital storage channels. Decision-feedback equalizers (DFEs) can decrease this nonlinear effect by including appropriate nonlinear feedback filters. Although various applications of these types of equalizers have been published in the literature, the analysis of their stability and error recovery has not appeared. We consider a DFE with a nonlinear feedback filter based on a discrete Volterra series. We extend error propagation, error probability, stability, and error recovery time results for Nth order nonlinear channelsTsimbinos, J. White, L.B
Limiting Performance of Conventional and Widely Linear DFT-precoded-OFDM Receivers in Wideband Frequency Selective Channels
This paper describes the limiting behavior of linear and decision feedback
equalizers (DFEs) in single/multiple antenna systems employing
real/complex-valued modulation alphabets. The wideband frequency selective
channel is modeled using a Rayleigh fading channel model with infinite number
of time domain channel taps. Using this model, we show that the considered
equalizers offer a fixed post signal-to-noise-ratio (post-SNR) at the equalizer
output that is close to the matched filter bound (MFB). General expressions for
the post-SNR are obtained for zero-forcing (ZF) based conventional receivers as
well as for the case of receivers employing widely linear (WL) processing.
Simulation is used to study the bit error rate (BER) performance of both MMSE
and ZF based receivers. Results show that the considered receivers
advantageously exploit the rich frequency selective channel to mitigate both
fading and inter-symbol-interference (ISI) while offering a performance
comparable to the MFB
Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels
The paper investigates adaptive equalization of time dispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE
MIMO decision feedback equalization from an H∞ perspective
We approach the multiple input multiple output (MIMO) decision feedback equalization (DFE) problem in digital communications from an H∞ estimation point of view. Using the standard (and simplifying) assumption that all previous decisions are correct, we obtain an explicit parameterization of all H∞ optimal DFEs. In particular, we show that, under the above assumption, minimum mean square error (MMSE) DFEs are H∞ optimal. The H∞ approach also suggests a method for dealing with errors in previous decisions
Linear MMSE-Optimal Turbo Equalization Using Context Trees
Formulations of the turbo equalization approach to iterative equalization and
decoding vary greatly when channel knowledge is either partially or completely
unknown. Maximum aposteriori probability (MAP) and minimum mean square error
(MMSE) approaches leverage channel knowledge to make explicit use of soft
information (priors over the transmitted data bits) in a manner that is
distinctly nonlinear, appearing either in a trellis formulation (MAP) or inside
an inverted matrix (MMSE). To date, nearly all adaptive turbo equalization
methods either estimate the channel or use a direct adaptation equalizer in
which estimates of the transmitted data are formed from an expressly linear
function of the received data and soft information, with this latter
formulation being most common. We study a class of direct adaptation turbo
equalizers that are both adaptive and nonlinear functions of the soft
information from the decoder. We introduce piecewise linear models based on
context trees that can adaptively approximate the nonlinear dependence of the
equalizer on the soft information such that it can choose both the partition
regions as well as the locally linear equalizer coefficients in each region
independently, with computational complexity that remains of the order of a
traditional direct adaptive linear equalizer. This approach is guaranteed to
asymptotically achieve the performance of the best piecewise linear equalizer
and we quantify the MSE performance of the resulting algorithm and the
convergence of its MSE to that of the linear minimum MSE estimator as the depth
of the context tree and the data length increase.Comment: Submitted to the IEEE Transactions on Signal Processin
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