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

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.

Submitted to JLT, currently under review

References in corpus (4)

• Mode-dependent Loss and Gain Estimation in SDM Transmission Based on MMSE Equalizers
• Experimental validation of MDL emulation and estimation techniques for SDM transmission systems
• Neural-network-based MDG and Optical SNR Estimation in SDM Transmission
• 1 Tbit/s/$\lambda$ Transmission Over a 130 km Link Consisting of Graded-Index 50 $\mu$m Core Multi-Mode Fiber and 6LP Few-Mode Fiber