Efficient channel modeling of structured light in turbulence using generative adversarial networks.

Area of Science:

• Optical Communications
• Atmospheric Optics
• Computational Physics

Background:

• Turbulent atmospheres disrupt free-space optics (FSO) communications, causing crosstalk between structured light modes.
• Accurate simulation of this crosstalk is crucial but computationally intensive with current phase-screen methods.
• Existing models face limitations in speed and accuracy for complex FSO systems.

Purpose of the Study:

• To present a novel, fast, and efficient simulation method for atmospheric turbulence effects on structured light in FSO.
• To overcome the computational burden and accuracy limitations of conventional simulation techniques.
• To introduce a data-driven approach utilizing conditional generative adversarial networks (CGANs).

Main Methods:

• A data-driven approach was employed for simulating the channel decomposition matrix.
• A conditional generative adversarial network (CGAN) was utilized as a synthetic simulator.
• The method focuses on modeling the crosstalk between higher-order modes in FSO.

Main Results:

• The proposed CGAN-based simulator provides a fast and efficient alternative for modeling FSO channel effects.
• This data-driven method circumvents the computational intensity of traditional phase-screen approximations.
• The simulation accurately captures turbulence-induced mode crosstalk.

Conclusions:

• A novel CGAN-based simulation method significantly enhances the efficiency and speed of modeling atmospheric turbulence in FSO.
• This approach offers a viable solution for overcoming the limitations of existing simulation techniques.
• The developed method paves the way for more practical and scalable FSO system analysis and design.