Modeling the Bit-Error-Rate Performance of Nonlinear Fiber-Optic Systems

Abstract

We present a detailed statistical model of nonlinear interference noise (NLIN) in optical communication systems. We demonstrate an efficient method of calculating 2nd order statistics of the NLIN coefficients, particularly their temporal autocorrelation and cross-correlation. The model is highly accurate in predicting system performance metrics such as bit-errorrate and signal-to-noise ratio, and is shown to provide better accuracy with respect to models that use the NLIN variance alone, particularly when accounting for the adaptive filtering of realistic receivers.

Experimental Characterization of the Time Correlation Properties of Nonlinear Interference Noise

“…As demonstrated in Refs. 3,8 , following matched filtering and digital dispersion compensation, the n-th sample of the received signal is given by…”

“…The ISI coefficients R (n) l are 2 × 2 matrices, whose elements are determined by the data transmitted over the interfering WDM channels. It has been demonstrated in [1][2][3] that the summation on the righthand-side of (1) accurately accounts for the NLIN that is generated by XPM. The index l in R (n) l represents the ISI order, whereas the superscript (n) points to the (relatively slow) dependence of the ISI matrices on time.…”

“…The index l in R (n) l represents the ISI order, whereas the superscript (n) points to the (relatively slow) dependence of the ISI matrices on time. The statistical properties of the ISI matrices have been studied theoretically in 3 . Our goal here is to show how they can be characterized experimentally in practical transmission systems.…”

“…In recent years, extensive efforts have been invested into the characterization of NLIN and the exploration of methods for its mitigation. An important finding in this context was that the cross-phase-modulation (XPM) component of NLIN (the predominant component in most practical scenarios) can be modeled very accurately as an inter-symbol interference (ISI) process with random and time varying ISI coefficients [1][2][3] . Hence, the characterization of NLIN can be translated into the characterization of the corresponding ISI coefficients.…”

Correlated Nonlinear Phase-Noise in Multi-Subcarrier Systems: Modeling and Mitigation

“…It has been shown that the relatively long correlation times that characterize NLPN can be used in order to effectively mitigate it by means of adaptive equalization [6]. However, SCM reduces the correlation length (in terms of number of symbols), making the use of adaptive equalization more challenging.…”