We use a deep learning neural network to successfully indicate when an eavesdropper is present in a free-space optics ON-OFF keying communication scheme, in which the bit stream has passed through turbulence.
We design a deep learning assisted end to end free-space optical ON-OFF Keying communications system, and demonstrate its efficacy in significantly lowering the bit error rate, while requiring no knowledge of the channel state information.
Evanescent waves are central to many technologies such as near-field imaging that beats the diffraction limit and plasmonic devices. Frustrated total internal reflection (FTIR) is an experimental method commonly used to study evanescent waves. In this paper, we shape the incident beam of the FTIR process with a Mach-Zehnder interferometer and measure light transmittance while varying the path length difference and interferometric visibility. Our results show that the transmittance varies with the path length difference and, thus, the intensity distribution of the shaped beam. Experiment and finite element method simulation produce results that agree. We also show, through simulations, that the transmittance can be controlled via other methods of beam shaping. Our work provides a proof-of-concept demonstration of the coherent control of the FTIR process, which could lead to advancements in numerous applications of evanescent waves and FTIR.
We demonstrate the efficacy of machine learning techniques in the detection of an eavesdropper in a free-space optical (FSO) communications setup. Experimentally, we use ON-OFF keying (OOK) and send strings of random bits through strong turbulence. When we apply a simulated eavesdropper to the bits in the post processing stage, a deep learning convolutional neural network (CNN) is able to successfully detect whether or not the eavesdropper is present. We vary the strength and duration of the attenuation of the simulated eavesdropper, and vary the signal-to-noise ratio (SNR) of the bit streams, and find that the strength of the eavesdropper has the greatest effect on eavesdropper detection accuracy. We are hopeful this flexible approach may be used in current and future operational FSO communications systems.
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