Multimodal Orbital Angular Momentum Data Model Based on Mechanically Reconfigurable Arrays and Neural Networks

Area of Science:

• Electromagnetic wave communication
• Optical physics
• Robotics and control systems

Background:

• Multimodal orbital angular momentum (OAM) is crucial for advanced electromagnetic wave communication.
• Accurate detection and identification of multimodal OAM data remain a significant challenge.
• Mechanically reconfigurable arrays and neural networks offer potential solutions for OAM manipulation and detection.

Purpose of the Study:

• To model and realize a transceiver system for multimodal OAM vortex waves.
• To investigate the purity, detection, transmission, and reception of OAM vortex waves.
• To improve the accuracy and efficiency of OAM signal processing and communication.

Main Methods:

• Utilizing mechanically reconfigurable arrays and neural network theory.
• Implementing a single neuron PID controller for manipulator control.
• Developing a three-transmitting and three-receiving OAM vortex wave transceiver system.

Main Results:

• Achieved a transmission coefficient of 0.827 between same-mode OAM signals.
• Demonstrated significantly higher transmission coefficients for same-mode versus different-mode signals.
• Reduced information delay to 8.25% and improved channel isolation characteristics.

Conclusions:

• The developed OAM transceiver system based on reconfigurable arrays and neural networks is effective.
• The system enhances the communication transmission of multimodal signals by improving modal purity and detection.
• The approach offers a promising direction for future high-capacity OAM communication systems.