The Memory-Enhanced Gaussian Noise (MEGN) Model for Fiber-Optic Channels

The enhanced Gaussian noise (EGN) model is widely used for estimating the nonlinear interference (NLI) power accumulated in coherent fiber-optic transmission systems. Given a fixed fiber link, under the assumption that transmitted symbols are independently and identically distributed (i.i.d.), the EGN model establishes that the NLI power depends on time-invariant signal statistics, i.e., the second-, fourth-, and sixth-order moments of the symbols, which are determined by the modulation format and its probability distribution. However, recent advances in coded modulation have sought to mitigate NLI by introducing controlled temporal correlations among transmitted symbols, thereby violating the i.i.d. assumption underlying the EGN model. Among these correlations, symbol energy correlations are believed to exert the most significant influence on NLI. This work presents a rigorous mathematical derivation of a memory extension of the EGN model that explicitly accounts for symbol energy correlations, referred to as the MEGN model. The proposed MEGN model is validated through both numerical simulations and transmission experiments. Normalized average NLI power estimations with less than 5% errors across a wide range of symbol rates and transmission distances are reported. The model also provides a theoretical framework for analyzing and optimizing optical transmission systems employing temporally correlated modulation schemes.

On Digital Subcarrier Multiplexing under A Bandwidth Limitation and ASE Noise

We show that digital subcarrier multiplexing (DSM) systems require much greater complexity for Nyquist pulse shaping than single-carrier (SC) systems, and it is a misconception that both systems use the same bandwidth when using the same pulse shaping. Through back-to-back (B2B) experiments with realistic transmitter (TX) modules and amplified spontaneous emission (ASE) noise loading, we show that even with optimized waterfilling and entropy loading, DSM does not achieve a larger net data rate (NDR) compared to SC when only ASE noise exists in the channel in long-haul transmission scenarios.

Supply-Power-Constrained Cable Capacity Maximization Using Deep Neural Networks

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.