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.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/OE.387668 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring are important portions of optical performance monitoring (OPM) for future dynamic optical networks. In this paper, we proposed a fusion module few-shot learning (FMFSL) algorithm as an improvement upon the ordinary few-shot learning algorithms for image recognition with the specialty in adopting a combination of a dilated convolutional group and an asymmetric convolutional group to advance the feature extraction. FMFSL algorithm is applied in MFI and OSNR monitoring in coherent optical communication systems with its performance investigated in both back-to-back and fiber transmission scenarios using small-scale constellation diagrams.
View Article and Find Full Text PDFAn algorithm is proposed for few-shot-learning (FSL) jointing modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation. The constellation diagrams of six widely-used modulation formats over a wide range of OSNR (10-40 dB) are obtained by a dual-polarization (DP) coherent detection system at 32 GBaud. We introduce auxiliary task to model-agnostic meta-learning (MAML) which makes the gradient of meta tasks decline faster in the direction of optimal target.
View Article and Find Full Text PDFOptical parameter estimation based on the data obtained by coherent optical receivers is critical for optical performance monitoring (OPM) and the stable operation of the receiver digital signal processing (DSP). A robust multi-parameter estimation is intricate due to the interference of various system effects. By resorting to the cyclostationary theory, we are able to formulate a chromatic dispersion (CD), frequency offset (FO), and optical signal-to-noise ratio (OSNR) joint estimation strategy that is resistant to the random polarization effect, including polarization mode dispersion (PMD) and polarization rotation.
View Article and Find Full Text PDFAn approach for simultaneous modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring in digital coherent optical communications is proposed based on optoelectronic reservoir computing (RC) and the signal's amplitude histograms (AHs) obtained after the adaptive post-equalization. The optoelectronic RC is implemented using a Mach-Zehnder modulator and optoelectronic delay feedback loop. We investigate the performance of the proposed model with the number of symbols, bins of AHs and the hyperparameters of optoelectronic RC.
View Article and Find Full Text PDFConvolutional neural network based transfer learning (TL) is proposed to achieve joint optical performance monitoring with bit rate and modulation format identification in optical communication systems. TL is used to improve the execution of various tasks by extracting features without knowing other optical link parameters. Eye diagrams of four different modulation formats are generated at optical signal-to-noise ratios (OSNRs) varying from 15 to 30 dB for two distinct bit rates, which are then identified simultaneously with a trained deep neural network.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!