Generative Machine Learning for Robust Free-Space Communication
This work addresses robust communication for free-space optical systems, offering a scalable and cost-effective solution that could benefit long-range classical and quantum links, though it appears incremental as it combines existing methods.
The paper tackled the problem of atmospheric turbulence and detector noise degrading free-space optical communications by developing a generative machine learning and convolutional neural network system, which significantly lowered symbol error ratios and cross-talk without requiring feedback.
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.