Multimode Fiber Transmission Matrix Inversion with Densely Connected Convolutional Network for Physical Layer Security

Qian Zhang; Stefan Rothe; Nektarios Koukourakis; Jürgen Czarske

doi:10.1364/CLEO_SI.2021.SM1B.2

Conference on Lasers and Electro-Optics
OSA Technical Digest (Optica Publishing Group, 2021),
paper SM1B.2
•https://doi.org/10.1364/CLEO_SI.2021.SM1B.2

Multimode Fiber Transmission Matrix Inversion with Densely Connected Convolutional Network for Physical Layer Security

Qian Zhang, Stefan Rothe, Nektarios Koukourakis, and Jürgen Czarske

CLEO: Science and Innovations 2021

San Jose, California United States
9–14 May 2021
ISBN: 978-1-943580-91-0

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Optical Machine Learning and Security (SM1B)

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Q. Zhang, S. Rothe, N. Koukourakis, and J. Czarske, "Multimode Fiber Transmission Matrix Inversion with Densely Connected Convolutional Network for Physical Layer Security," in Conference on Lasers and Electro-Optics, J. Kang, S. Tomasulo, I. Ilev, D. Müller, N. Litchinitser, S. Polyakov, V. Podolskiy, J. Nunn, C. Dorrer, T. Fortier, Q. Gan, and C. Saraceno, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper SM1B.2.

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Abstract

For exploiting multimode fiber optic communication networks towards physical layer security, we have trained a neural network performing mode decomposition of 10 modes. The approach is based on intensity-only camera images and works in real-time.

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