Engineering

Optical fiber

Modulation (music)

Quadrature amplitude modulation

Telecommunications

Computer science

Acoustics

Wavelength

Physics

Pattern recognition (psychology)

Optics

Optical performance monitoring

Bit error rate

Electronic engineering

Polarization mode dispersion

Artificial intelligence

Channel (broadcasting)

Wavelength-division multiplexing

Convolutional neural network

Optical Engineering

We propose an ensemble deep transfer learning (EDTL) method, which is a more refined multilayer feature extraction achieved by aggregating the convolutional layers of pretrained convolutional neural network models for joint optical modulation format recognition (MFR) and optical performance monitoring (OPM) in fiber-optic communication links. Modulation formats, such as quadrature phase shift keying, 16 quadrature amplitude modulation (QAM), and 64 QAM, are monitored for the optical signal-to-noise ratio (OSNR) range of 20 to 30 dB by considering the dispersive effects of chromatic dispersion from 0 to 1200 ps / nm and polarization mode dispersion from 10 to 70 ps in the fiber-optic transmission path. First, the generated constellation diagrams affected by the impairments are used to optimize and evaluate the pretrained models based on classification targets. Then the proposed EDTL model is designed by aggregating the feature extractor parts of the pretrained models; it is implemented in three phases, and the results are comprehensively studied. Further, data augmentation and aggregation methods are introduced to enhance the performance of joint MFR and OPM. The results obtained prove that the proposed model provides faster convergence of MFR and better identification accuracy of OSNR toward optical signal diagnostics in optical networks for efficient optical link monitoring.

Joint modulation format recognition and optical performance monitoring for efficient fiber-optic communication links using ensemble deep transfer learning