Transfer learning assisted deep neural network for OSNR estimation.

Area of Science:

• Optical Communications
• Machine Learning
• Signal Processing

Background:

• Accurate optical signal-to-noise ratio (OSNR) monitoring is crucial for maintaining network performance.
• Traditional OSNR monitoring methods can be slow and computationally intensive, especially under dynamic system conditions.
• Deep neural networks (DNNs) offer potential for advanced signal monitoring but require substantial training data and resources.

Purpose of the Study:

• To propose a novel transfer learning-assisted DNN method for efficient OSNR monitoring.
• To enable fast system parameter remodeling for dynamic optical networks.
• To reduce computational complexity and data requirements for OSNR estimation.

Main Methods:

• Utilized a deep neural network (DNN) architecture for OSNR estimation.
• Employed transfer learning by transferring DNN hyper-parameters to accelerate adaptation.
• Used amplitude histograms of 56-Gb/s QPSK signals as input features for the DNN.
• Evaluated performance based on root mean squared error (RMSE) and resource savings.

Main Results:

• Achieved a root mean squared error (RMSE) of less than 0.1 dB for OSNR estimation across a range of 5 to 35 dB.
• Demonstrated significant resource savings, with a four-fold reduction in training epochs and a five-fold reduction in training set size.
• Showcased superior fast remodeling capabilities in response to changes in optical launch power, chromatic dispersion, and bit rate.

Conclusions:

• The proposed transfer learning-assisted DNN method provides an efficient and accurate approach for OSNR monitoring.
• This technique significantly reduces computational and data resource requirements, making it suitable for real-time applications.
• The ability to rapidly remodel for system variations enhances the robustness and applicability of the method in dynamic optical networks.