1,371 research outputs found

    Time varying ISI model for nonlinear interference noise

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    We show that the effect of nonlinear interference in WDM systems is equivalent to slowly varying inter-symbol-interference (ISI), and hence its cancellation can be carried out by means of adaptive linear filtering. We characterize the ISI coefficients and discuss the potential gain following from their cancellation.Comment: Submitted to the optical fiber communication conference (OFC), 201

    Polarization Drift Channel Model for Coherent Fibre-Optic Systems

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    A theoretical framework is introduced to model the dynamical changes of the state of polarization during transmission in coherent fibre-optic systems. The model generalizes the one-dimensional phase noise random walk to higher dimensions, accounting for random polarization drifts, emulating a random walk on the Poincar\'e sphere, which has been successfully verified using experimental data. The model is described in the Jones, Stokes and real four-dimensional formalisms, and the mapping between them is derived. Such a model will be increasingly important in simulating and optimizing future systems, where polarization-multiplexed transmission and sophisticated digital signal processing will be natural parts. The proposed polarization drift model is the first of its kind as prior work either models polarization drift as a deterministic process or focuses on polarization-mode dispersion in systems where the state of polarization does not affect the receiver performance. We expect the model to be useful in a wide-range of photonics applications where stochastic polarization fluctuation is an issue.Comment: 15 pages, 4 figure

    Improved Lower Bounds on Mutual Information Accounting for Nonlinear Signal-Noise Interaction

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    In fiber-optic communications, evaluation of mutual information (MI) is still an open issue due to the unavailability of an exact and mathematically tractable channel model. Traditionally, lower bounds on MI are computed by approximating the (original) channel with an auxiliary forward channel. In this paper, lower bounds are computed using an auxiliary backward channel, which has not been previously considered in the context of fiber-optic communications. Distributions obtained through two variations of the stochastic digital backpropagation (SDBP) algorithm are used as auxiliary backward channels and these bounds are compared with bounds obtained through the conventional digital backpropagation (DBP). Through simulations, higher information rates were achieved with SDBP, {which can be explained by the ability of SDBP to account for nonlinear signal--noise interactionsComment: 8 pages, 5 figures, accepted for publication in Journal of Lightwave Technolog

    Practical Detection Schemes for Power Efficient Modulation Formats

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    We discuss the detection process for a selection of power efficient formats. The receiver subsystems of dynamic equalization, phase estimation, and data detection are described with emphasis on the case with differential encoding

    Perturbation Analysis of Nonlinear Propagation in a Strongly Dispersive Optical Communication System

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    We discuss an analytical model that predicts the impact of the Kerr nonlinearity in optical communication systems when the signal spectrum is wide and the accumulated dispersion during propagation is large. A detailed derivation of this model is given for a generalized system by means of a perturbation analysis of the Manakov equation with attenuation, gain, and third order dispersion included. As in the case with previous studies, three simplifying assumptions are necessary. These are that (i) the nonlinearity is weak, (ii) the input signal is of a given specific form, and (iii) the signal-noise interaction can be neglected. Under these assumptions, the result is found exactly. We also discuss the accuracy of the analytical result and show that third order dispersion has a small impact in practice

    Modeling of Nonlinear Signal Distortion in Fiber-Optic Networks

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    A low-complexity model for signal quality prediction in a nonlinear fiber-optic network is developed. The model, which builds on the Gaussian noise model, takes into account the signal degradation caused by a combination of chromatic dispersion, nonlinear signal distortion, and amplifier noise. The center frequencies, bandwidths, and transmit powers can be chosen independently for each channel, which makes the model suitable for analysis and optimization of resource allocation and routing in large-scale optical networks applying flexible-grid wavelength-division multiplexing

    Stochastic Digital Backpropagation with Residual Memory Compensation

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    Stochastic digital backpropagation (SDBP) is an extension of digital backpropagation (DBP) and is based on the maximum a posteriori principle. SDBP takes into account noise from the optical amplifiers in addition to handling deterministic linear and nonlinear impairments. The decisions in SDBP are taken on a symbol-by-symbol (SBS) basis, ignoring any residual memory, which may be present due to non-optimal processing in SDBP. In this paper, we extend SDBP to account for memory between symbols. In particular, two different methods are proposed: a Viterbi algorithm (VA) and a decision directed approach. Symbol error rate (SER) for memory-based SDBP is significantly lower than the previously proposed SBS-SDBP. For inline dispersion-managed links, the VA-SDBP has up to 10 and 14 times lower SER than DBP for QPSK and 16-QAM, respectively.Comment: 7 pages, accepted to publication in 'Journal of Lightwave Technology (JLT)

    A Low-Complexity Detector for Memoryless Polarization-Multiplexed Fiber-Optical Channels

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    A low-complexity detector is introduced for polarization-multiplexed M-ary phase shift keying modulation in a fiber-optical channel impaired by nonlinear phase noise, generalizing a previous result by Lau and Kahn for single-polarization signals. The proposed detector uses phase compensation based on both received signal amplitudes in conjunction with simple straight-line rather than four-dimensional maximum-likelihood decision boundaries.Comment: accepted for publication in IEEE Comm. Let
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