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• W4285301852 abstract "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." @default.
• W4285301852 created "2022-07-14" @default.
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• W4285301852 date "2022-08-01" @default.
• W4285301852 modified "2023-10-16" @default.
• W4285301852 title "MDG and SNR Estimation in SDM Transmission Based on Artificial Neural Networks" @default.
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• W4285301852 doi "https://doi.org/10.1109/jlt.2022.3174778" @default.
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