Encrypted information transmission via high-resolution elliptical vortex modes based on deep learning.

Area of Science:

• Optical physics
• Information optics
• Machine learning applications

Background:

• Elliptical vortex beams possess two degrees of freedom: topological charge and ellipticity.
• These properties enhance information transmission capacity and processing in optical communication.
• Accurate detection of these properties is vital for realizing the full potential of elliptical vortex beams.

Purpose of the Study:

• To develop an accurate method for identifying the topological charge and ellipticity of elliptical vortex beams.
• To demonstrate the effectiveness of the proposed method in a practical optical communication system.

Main Methods:

• An improved ResNet architecture based on convolutional neural networks was developed.
• The method was trained to identify high-resolution interference patterns of elliptical vortex beams.
• An elliptical vortex beam multiplexing encryption system was utilized for data transmission tests.

Main Results:

• The ResNet achieved 88.12% recognition accuracy for topological charge (resolution 0.01) and 99.85% for ellipticity (resolution 0.1).
• Image transmission using the elliptical vortex beam system demonstrated high accuracy (99.64% for a cat image at 0.1 topological charge resolution).

Conclusions:

• The proposed ResNet-based method enables accurate detection of elliptical vortex beam degrees of freedom.
• This approach has significant implications for free-space optical communication utilizing orbital angular momentum.
• The findings offer new perspectives for the manipulation and application of elliptical vortex beams.