Predicting Nonlinear Interference for Short-Blocklength 4D Probabilistic Shaping
Jingxin Deng, Bin Chen, Zhiwei Liang, Yi Lei, Gabriele Liga
TL;DR
This paper introduces a heuristic nonlinear interference model for 4D probabilistic shaping that accounts for polarization and time correlation, achieving high accuracy in SNR prediction compared to simulations.
Contribution
The paper presents a novel nonlinear interference model specifically designed for 4D probabilistic shaping, incorporating polarization and time correlation effects.
Findings
Average SNR prediction gap of 0.15 dB from simulations
Model effectively captures nonlinear interference in 4D probabilistic shaping
Provides a practical tool for system performance estimation
Abstract
We derive a heuristic nonlinear interference model for 4D probabilistic shaping considering the polarization and time correlation of the 4D symbols. We demonstrate an average SNR prediction gap from split-step Fourier simulations of 0.15~dB.
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