Abstract

In this Letter, we propose a generalized optical signal-to-noise ratio (GOSNR) monitoring scheme using a convolutional neural network trained on constellation density features acquired from a back-to-back setup and demonstrate accurate GOSNR estimations for links having different nonlinearities. The experiments were carried over dense wavelength division multiplexing links configured on 32-Gbaud polarization division multiplexed 16-quadrature amplitude modulation (QAM) and demonstrated that the GOSNRs are estimated within 0.1 dB mean absolute error with maximum estimation errors below 0.5 dB on metro class links. The proposed technique does not require any information about the noise floor in the conventional spectrum-based means and therefore is readily deployable for real-time monitoring.

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