Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

Structured light with spatial degrees of freedom (DoF) is considered a potential solution to address the unprecedented demand for data traffic, but there is a limit to effectively improving the communication capacity by its integer quantization. We propose a data transmission system using fractional mode encoding and deep-learning decoding. Spatial modes of Bessel-Gaussian beams separated by fractional intervals are employed to represent 8-bit symbols. Data encoded by switching phase holograms is efficiently decoded by a deep-learning classifier that only requires the intensity profile of transmitted modes. Our results show that the trained model can simultaneously recognize two independent DoF without any mode sorter and precisely detect small differences between fractional modes. Moreover, the proposed scheme successfully achieves image transmission despite its densely packed mode space. This research will present a new approach to realizing higher data rates for advanced optical communication systems.

Automated summary

This study proposes an efficient data transmission system that achieves higher data rates using fractional mode encoding and deep-learning decoding. By representing symbols using spatial modes and decoding efficiently with a deep-learning algorithm, the system successfully achieves image transmission while recognizing two independent degrees of freedom simultaneously.