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

Ruby Stella Bravo Ospina (Corresponding author), Menno van den Hout, Sjoerd van der Heide, John van Weerdenburg, Roland Ryf, Nicolas Keith Fontaine, Haoshuo Chen, Rodrigo Amezcua Correa, Chigo M. Okonkwo, Darli A.A. Mello

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

3 Citaten (Scopus)
99 Downloads (Pure)


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 signal-to-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 multiple-input 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.

Originele taal-2Engels
Pagina's (van-tot)5021-5030
Aantal pagina's10
TijdschriftJournal of Lightwave Technology
Nummer van het tijdschrift15
StatusGepubliceerd - 1 aug. 2022


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