1,684 research outputs found
Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders
We investigate the potential of adaptive blind equalizers based on
variational inference for carrier recovery in optical communications. These
equalizers are based on a low-complexity approximation of maximum likelihood
channel estimation. We generalize the concept of variational autoencoder (VAE)
equalizers to higher order modulation formats encompassing probabilistic
constellation shaping (PCS), ubiquitous in optical communications, oversampling
at the receiver, and dual-polarization transmission. Besides black-box
equalizers based on convolutional neural networks, we propose a model-based
equalizer based on a linear butterfly filter and train the filter coefficients
using the variational inference paradigm. As a byproduct, the VAE also provides
a reliable channel estimation. We analyze the VAE in terms of performance and
flexibility over a classical additive white Gaussian noise (AWGN) channel with
inter-symbol interference (ISI) and over a dispersive linear optical
dual-polarization channel. We show that it can extend the application range of
blind adaptive equalizers by outperforming the state-of-the-art
constant-modulus algorithm (CMA) for PCS for both fixed but also time-varying
channels. The evaluation is accompanied with a hyperparameter analysis.Comment: Published (Open Access) in IEEE Journal on Selected Areas in
Communications, Sep 202
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
Integrated Transversal Equalizers in High-Speed Fiber-Optic Systems
Intersymbol interference (ISI) caused by intermodal dispersion in multimode fibers is the major limiting factor in the achievable data rate or transmission distance in high-speed multimode fiber-optic links for local area networks applications. Compared with optical-domain and other electrical-domain dispersion compensation methods, equalization with transversal filters based on distributed circuit techniques presents a cost-effective and low-power solution. The design of integrated distributed transversal equalizers is described in detail with focus on delay lines and gain stages. This seven-tap distributed transversal equalizer prototype has been implemented in a commercial 0.18-µm SiGe BiCMOS process for 10-Gb/s multimode fiber-optic links. A seven-tap distributed transversal equalizer reduces the ISI of a 10-Gb/s signal after 800 m of 50-µm multimode fiber from 5 to 1.38 dB, and improves the bit-error rate from about 10^-5 to less than 10^-12
Diffusive MIMO Molecular Communications: Channel Estimation, Equalization and Detection
In diffusion-based communication, as for molecular systems, the achievable
data rate is low due to the stochastic nature of diffusion which exhibits a
severe inter-symbol-interference (ISI). Multiple-Input Multiple-Output (MIMO)
multiplexing improves the data rate at the expense of an inter-link
interference (ILI). This paper investigates training-based channel estimation
schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization
methods. Maximum likelihood and least-squares estimators of mean channel are
derived, and the training sequence is designed to minimize the mean square
error (MSE). Numerical validations in terms of MSE are compared with Cramer-Rao
bound derived herein. Equalization is based on decision feedback equalizer
(DFE) structure as this is effective in mitigating diffusive ISI/ILI.
Zero-forcing, minimum MSE and least-squares criteria have been paired to DFE,
and their performances are evaluated in terms of bit error probability. Since
D-MIMO systems are severely affected by the ILI because of short transmitters
inter-distance, D-MIMO time interleaving is exploited as countermeasure to
mitigate the ILI with remarkable performance improvements. The feasibility of a
block-type communication including training and data equalization is explored
for D-MIMO, and system-level performances are numerically derived.Comment: Accepted paper at IEEE transaction on Communicatio
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