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

Autor(es)

Ospina, Ruby S. B. ; van den Hout, Menno ; van der Heide, Sjoerd ; van Weerdenburg, John ; Ryf, Roland ; Fontaine, Nicolas K. ; Chen, Haoshuo ; Amezcua-Correa, Rodrigo ; Okonkwo, Chigo ; Mello, Darli A. A.

• Tipo de documento: Artigo Científico
• Fonte: Journal of Lightwave Technology; v. 40, n. 15, p. 10-pg., 2022-08-01.

Resumo

The increase in capacity provided by coupled space division multiplexing (SDM) systems is fundamentally limited by mode-dependent gain (MDG) and amplified spontaneous emission (ASE) noise. Therefore, monitoring MDG and optical signalto-noise ratio (SNR) is essential for accurate performance evaluation and troubleshooting. Recent works show that the conventional MDG estimation method based on the transfer matrix of multipleinput multiple-output(MIMO) equalizers optimizing the minimum mean square error (MMSE) underestimates the actual value at low SNRs. 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 artificial neural networks (ANNs). The proposed ANN-based method processes features extracted at the receiver after digital signal processing (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. (AU)

• Processo FAPESP: 17/25537-8 - Modulação com adaptação à distância em sistemas ópticos com multiplexação por divisão espacial
• Beneficiário: Ruby Stella Bravo Ospina
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• Processo FAPESP: 18/14026-5 - Projeto e otimização de enlaces ópticos submarinos usando multiplexação por divisão espacial
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