MDG and SNR Estimation in SDM Transmission Based on Artificial Neural Networks

TL;DR

This paper presents an artificial neural network-based method for accurately estimating mode-dependent gain and optical SNR in SDM transmission systems, validated through experiments on 3-mode and 6-mode links.

Contribution

It introduces a novel ANN-based approach for joint MDG and SNR estimation that outperforms traditional methods in SDM systems.

Findings

ANN estimates MDG and SNR with high accuracy

The method outperforms conventional estimation techniques

Validated on 73-km and long-haul 6-mode links

Abstract

The increase in capacity provided by coupled SDM systems is fundamentally limited by MDG and ASE noise. Therefore, monitoring MDG and optical SNR is essential for accurate performance evaluation and troubleshooting. Recent works show that the conventional MDG estimation method based on the transfer matrix of MIMO equalizers optimizing the MMSE underestimates the actual value at low SNR. Besides, estimating the optical SNR itself is not a trivial task in SDM systems, as MDG strongly influences the electrical SNR after the equalizer. In a recent work we propose an MDG and SNR estimation method using ANN. The proposed ANN-based method processes features extracted at the receiver after DSP. In this paper, we discuss the ANN-based method in detail, and validate it in an experimental 73-km 3-mode transmission link with controlled MDG and SNR. After validation, we apply the method in a case study consisting of an experimental long-haul 6-mode link. The results show that the ANN estimates both MDG and SNR with high accuracy, outperforming conventional methods.