Neural-network-based MDG and Optical SNR Estimation in SDM Transmission

TL;DR

This paper introduces a neural network model that accurately estimates mode-dependent gain and optical SNR in SDM transmission, offering a low-complexity solution based on features extracted after digital signal processing.

Contribution

The paper presents a novel neural network approach for MDG and SNR estimation in SDM transmission, improving accuracy and reducing complexity compared to existing methods.

Findings

High accuracy in MDG and SNR estimation

Low complexity of the neural network solution

Effective feature extraction after DSP

Abstract

We propose a neural network model for MDG and optical SNR estimation in SDM transmission. We show that the proposed neural-network-based solution estimates MDG and SNR with high accuracy and low complexity from features extracted after DSP.