Combined image Hough transform based simultaneous multi-parameter optical performance monitoring for intelligent optical networks

Abstract

The fast-emerging flexible optical networks require intelligent monitoring techniques for supervising the signal quality performance to achieve the desired efficiency. In this paper, we propose a multi-task convolutional neural network (MT-CNN) for multi-parameter monitoring using a unique combined image Hough Transform (CIHT) technique. The proposed system supports simultaneous modulation format identification (MFI), transmission distance detection (TDD), roll-off factor identification (ROFI), Laser linewidth detection (LLWD), centre wavelength detection (CWLD) along with the optical signal-to-noise ratio (OSNR) estimation. The proposed scheme realizes the multi-parameter optical performance monitoring using the advantage of Hough transform (HT) for efficient feature extraction. Rigorous simulations to prove the advantage of using Hough transform, and data augmentation has been performed to conclude the results achieved. The simulation results demonstrate 100% accuracy after 112 epochs for MFI, TDD, ROFI, LLWD, and CWLD, and the mean absolute error (MAE) for OSNR estimation has reduced to 0.354 dB after 180 epochs. Also, rigorous simulations with various conditions are performed to demonstrate the merits of the proposed method.