paper-with-me

Papers

Revisiting the nonlinear Gaussian noise model: The case of hybrid fiber spans

2020-09-14 · I. Roudas, J. Kwapisz, X. Jiang

We rederive from first principles and generalize the theoretical framework of the nonlinear Gaussian noise model to the case of coherent optical systems with multiple fiber types per span and ideal Nyquist spectra. We focus on the accurate numerical evaluation of the integral for the nonlinear noise variance for hybrid fiber spans. This task consists in addressing four computational aspects: (i) Adopting a novel transformation of variables (other than using hyperbolic coordinates) that changes the integrand to a more appropriate form for numerical quadrature; (ii) Evaluating analytically the integral at its lower limit, where the integrand presents a singularity; (iii) Dividing the interval of integration into subintervals of size pi and approximating the integral in each subinterval by using various algorithms; and (iv) Deriving an upper bound for the relative error when the interval of integration is truncated in order to accelerate computation. We apply the proposed model to coherent optical communications systems with hybrid fiber spans composed of quasi-singlemode fiber and single-mode fiber segments. The accuracy of the final analytical relationship for the nonlinear noise variance in long-haul coherent optical communications systems with hybrid fiber spans is checked using the split-step Fourier method and Monte Carlo simulation. It is shown to be adequate to within 0.1 dBQ for the determination of the optimal fiber segment lengths per span that maximize system performance.

📄 PDF Abstract BibTeX arXiv:2009.06126

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

The Gaussian Noise Model in the Presence of Inter-channel Stimulated Raman Scattering

2017-12-31

A Gaussian noise (GN) model is presented that properly accounts for an arbitrary frequency dependent signal power profile along the link. This enables the evaluation of the impact of inter-channel stimulated Raman scatte…

Beyond Weighted Summation: Learnable Nonlinear Aggregation Functions for Robust Artificial Neurons

2026-03-19 · Berke Deniz Bozyigit arxiv

Weighted summation has remained the default input aggregation mechanism in artificial neurons since the earliest neural network models. While computationally efficient, this design implicitly behaves like a mean-based es…

On Causal Discovery with Cyclic Additive Noise Models

2011-12-01 · NeurIPS 2011 12 · Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf

We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise. We prove that the causal graph of such models is generically identifiable in …

Causal Discoveryregression

Nonlinear directed acyclic structure learning with weakly additive noise models

2009-12-01 · NeurIPS 2009 12 · Arthur Gretton, Peter Spirtes, Robert E. Tillman

The recently proposed \emph{additive noise model} has advantages over previous structure learning algorithms, when attempting to recover some true data generating mechanism, since it (i) does not assume linearity or Gaus…

Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure

2024-10-31 · Xiang Li, Yixiang Dai, Qing Qu

In this work, we study the generalizability of diffusion models by looking into the hidden properties of the learned score functions, which are essentially a series of deep denoisers trained on various noise levels. We o…

Inductive BiasMemorization