880 research outputs found
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
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
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
Inter-carrier interference mitigation for underwater acoustic communications
Communicating at a high data rate through the ocean is challenging. Such communications must be acoustic in order to travel long distances. The underwater acoustic channel has a long delay spread, which makes orthogonal frequency division multiplexing (OFDM) an attractive communication scheme. However, the underwater acoustic channel is highly dynamic, which has the potential to introduce significant inter-carrier interference (ICI). This thesis explores a number of means for mitigating ICI in such communication systems. One method that is explored is directly adapted linear turbo ICI cancellation. This scheme uses linear filters in an iterative structure to cancel the interference. Also explored is on-off keyed (OOK) OFDM, which is a signal designed to avoid ICI
Bacterial Foraging Based Channel Equalizers
A channel equalizer is one of the most important subsystems in any digital
communication receiver. It is also the subsystem that consumes maximum computation
time in the receiver. Traditionally maximum-likelihood sequence estimation (MLSE) was
the most popular form of equalizer. Owing to non-stationary characteristics of the
communication channel MLSE receivers perform poorly. Under these circumstances
‘Maximum A-posteriori Probability (MAP)’ receivers also called Bayesian receivers
perform better.
Natural selection tends to eliminate animals with poor “foraging strategies” and favor the
propagation of genes of those animals that have successful foraging strategies since they
are more likely to enjoy reproductive success. After many generations, poor foraging
strategies are either eliminated or shaped into good ones (redesigned). Logically, such
evolutionary principles have led scientists in the field of “foraging theory” to
hypothesize that it is appropriate to model the activity of foraging as an optimization
process.
This thesis presents an investigation on design of bacterial foraging based channel
equalizer for digital communication. Extensive simulation studies shows that the
performance of the proposed receiver is close to optimal receiver for variety of channel
conditions. The proposed receiver also provides near optimal performance when channel
suffers from nonlinearities
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe
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