Interferometric orbital angular momentum mode detection in turbulence with deep learning.

Orbital angular momentum (OAM) modes are topical due to their versatility, and they have been used in several applications including free-space optical communication systems. The classification of OAM modes is a common requirement, and there are several methods available for this. One such method makes use of deep learning, specifically convolutional neural networks, which distinguishes between modes using their intensities. However, OAM mode intensities are very similar if they have the same radius or if they have opposite topological charges, and as such, intensity-only approaches cannot be used exclusively for individual modes. Since the phase of each OAM mode is unique, deep learning can be used in conjugation with interferometry to distinguish between different modes. In this paper, we demonstrate a very high classification accuracy of a range of OAM modes in turbulence using a shear interferometer, which crucially removes the requirement of a reference beam. For comparison, we show only marginally higher accuracy with a more conventional Mach-Zehnder interferometer, making the technique a promising candidate towards real-time, low-cost modal decomposition in turbulence.

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

We present a fast and efficient simulation method of structured light free space optics (FSO) channel effects from propagation through a turbulent atmosphere. In a system that makes use of multiple higher order modes (structured light), turbulence causes crosstalk between modes. This crosstalk can be described by a channel matrix, which usually requires a complete physical simulation or an experiment. Current simulation techniques based on the phase-screen approximation method are very computationally intensive and are limited by the accuracy of the underlying models. In this work, we propose to circumvent these limitations by using a data-driven approach for the decomposition matrix simulation with a conditional generative adversarial network (CGAN) synthetic simulator.

Converting a Texas Instruments DLP4710 DLP evaluation module into a spatial light modulator.

Digital micro-mirror devices (DMDs) are a popular alternative to liquid crystal spatial light modulators for laser beam shaping due to their relatively low cost, high speed, and polarization and wavelength independence. Here we describe in detail how to convert a low-cost digital light projector (DLP) evaluation module that uses a Texas Instruments DLP4710 DMD into a spatial light modulator using a 3D printed mount. The resulting device is shown to accurately shape Laguerre-Gauss modes, is able to operate in real-time over HDMI without modification with a 180 Hz hologram refresh rate, and has a resolution of 1920×1080 pixels and diagonal screen size of 0.47 inches (11.9 mm).