Generative Machine Learning for Robust Free-Space Communication

TL;DR

This paper introduces a generative machine learning and CNN-based system that effectively corrects atmospheric turbulence and detector noise in free-space optical communications, significantly reducing error rates and enabling scalable long-range links.

Contribution

The paper presents a novel combination of GML and CNN techniques to improve free-space optical communication robustness without feedback, advancing the state-of-the-art.

Findings

Lowered symbol error ratios (SERs) in turbulent conditions

Reduced cross-talk between modes

No feedback required for correction

Abstract

Realistic free-space optical communications systems suffer from turbulent propagation of light through the atmosphere and detector noise at the receiver, which can significantly degrade the optical mode quality of the received state, increase cross-talk between modes, and correspondingly increase the symbol error ratio (SER) of the system. In order to overcome these obstacles, we develop a state-of-the-art generative machine learning (GML) and convolutional neural network (CNN) system in combination, and demonstrate its efficacy in a free-space optical (FSO) communications setting. The system corrects for the distortion effects due to turbulence and reduces detector noise, resulting in significantly lowered SERs and cross-talk at the output of the receiver, while requiring no feedback. This scheme is straightforward to scale, and may provide a concrete and cost effective technique to establishing long range classical and quantum communication links in the near future.