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Identifying modulation formats through 2D Stokes planes with deep neural networks.

Wenbo Zhang, Dingcheng Zhu, Zihang He

|September 7, 2018

Summary

A new deep learning method uses convolutional neural networks (CNNs) to identify modulation formats in polarization-division multiplexing (PDM) systems. This computer vision approach enhances signal identification accuracy in fiber communications.

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Area of Science:

Optical Communications
Signal Processing
Artificial Intelligence

Background:

Polarization-division multiplexing (PDM) systems are crucial for high-capacity fiber optic communications.
Accurate modulation format identification (MFI) is essential for reliable data transmission in PDM systems.
Existing MFI techniques face challenges in complex communication environments.

Purpose of the Study:

To propose and demonstrate a lightweight convolutional neural network (CNN)-based MFI scheme.
To analyze the impact of CNN learning rates on MFI performance.
To validate the proposed scheme in a practical PDM fiber communication system.

Main Methods:

Development of a CNN model for MFI using 2D Stokes planes.
Training the CNN with signal data from a 28GBaud PDM system.
Experimental verification and performance evaluation of the proposed MFI scheme.

Main Results:

Successfully identified six modulation formats: PDM-BPSK, PDM-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM.
Demonstrated significant improvement in identification performance compared to existing techniques.
Showcased the effectiveness of computer vision principles in MFI.

Conclusions:

The proposed lightweight CNN-based MFI scheme offers a robust solution for PDM systems.
The study highlights the potential of deep learning and computer vision for advancing optical communication technologies.
Further research can explore optimization of CNN architectures and learning parameters for enhanced MFI.