We investigate via experiments and simulations the statistical properties and the accumulation of nonlinear transmission impairments in coherent systems without optical dispersion compensation. We experimentally show that signal distortion due to Kerr nonlinearity can be modeled as additive Gaussian noise, and we demonstrate that its variance has a supra-linear dependence on propagation distance for 100 Gb/s transmissions over both low dispersion and standard single mode fiber. We propose a simple empirical model to account for linear and nonlinear noise accumulation, and to predict system performance for a wide range of distances, signal powers and optical noise levels.

No abstract

Abstract-In this paper, we show that the nonlinear parametric gain (PG) interaction between signal and noise is a nonnegligible factor in the design and analysis of long-haul dispersion-managed optical 10-Gb/s ON-OFF keying nonreturn to zero transmission systems operated at small signal-to-noise ratios (OSNRs) such as those employing forward-error correction (FEC) coding. In such a regime, we show that the in-phase noise spectrum exhibits a large gain close to the carrier frequency, which is due to the higher order noise terms accounting for the noise-noise beating during propagation that is usually neglected in the nonlinear Schrödinger equation. With a novel stochastic analysis that keeps such higher order terms, we are able to analytically quantify the maximum tolerable signal power after which PG unacceptably degrades system performance. We verify such an analytical power threshold by both simulation and experiment. We finally quantify the needed extra OSNR, or equivalently FEC coding gain, required when taking PG into account.

We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using machine learning by deep neural networks in a massively parallel fiber context. Gradient Descent DNN Launch Power Allocation Received Signal, Noise Powers

No abstract