We propose and experimentally demonstrate modulation format-independent optical performance monitoring (OPM) based on a multi-task artificial neural network (MT-ANN). Optical power measurements at a series of center wavelengths adjusted using a widely tunable optical bandpass filter (OBPF) are used as the input features for a MT-ANN to simultaneously realize high-precision optical signal-to-noise ratio (OSNR) and launch power monitoring and baud rate identification (BRI). This technique is insensitive to chromatic dispersion (CD) and polarization mode dispersion (PMD). The experimental verification in a 9-channel WDM system shows that for 10 Gbaud QPSK and 32 Gbaud PDM-16QAM signals with OSNR in the range of 1–30 dB, the OSNR mean absolute error (MAE) and root mean square error (RMSE) are 0.28 dB and 0.48 dB, respectively. For launch power in the range of 0–8 dBm, the MAE and RMSE of the launch power monitoring are 0.034 dB and 0.066 dB, respectively, and the identification accuracy for both baud rates is 100%. Furthermore, this technique utilizes a single MT-ANN instead of three ANNs to realize the simultaneous monitoring of three OPM parameters, which greatly reduces the cost and complexity.

We propose and experimentally demonstrate an accurate modulation-format-indepen-dent and cascaded filtering effect (CFE) insensitive in-band optical signal-to-noise ratio (OSNR) monitoring technique enabled by Gaussian process regression (GPR) utilizing a widely tunable optical bandpass filter (OBPF) and optical power measurements. By adjusting the center frequency of a widely tunable OBPF and measuring the corresponding output optical power as the input features of GPR, the proposed OSNR monitoring technique is experimentally proven to be transparent to modulation formats and robust to CFE, chromatic dispersion (CD), polarization mode dispersion (PMD), and nonlinear effect (NLE). Experimental results for 9-channel 32Gbaud PDM-16QAM signals with 50GHz channel spacing demonstrate OSNR monitoring with the root mean squared error (RMSE) of 0.429 dB and the mean absolute error (MAE) of 0.294 dB, in the OSNR range of -1∼30 dB. Even better, our proposed technique has the potential to be employed for link monitoring at the intermediation nodes and can eliminate the necessity to know the transmission information.

A 106 Gbit/s PAM4 transmission is experimentally demonstrated based on a DML. Received power of -3 dBm with 7% overhead HD-FEC is achieved by employing a digital pre-compensation and nonlinear equalization.