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Intensity-only Mode Decomposition on Multimode Fibers using a Densely Connected Convolutional Network

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Document pages: 17 pages

Abstract: The use of multimode fibers offers advantages in the field of communicationtechnology in terms of transferable information density and informationsecurity. For applications using physical layer security or mode divisionmultiplexing, the complex transmission matrix must be known. To measure thetransmission matrix, the individual modes of the multimode fiber are excitedsequentially at the input and a mode decomposition is performed at the output.Mode decomposition is usually performed using digital holography, whichrequires the provision of a reference wave and leads to high efforts. Toovercome these drawbacks, a neural network is proposed, which performs modedecomposition with intensity-only camera recordings of the multimode fiberfacet. Due to the high computational complexity of the problem, this approachwas usually limited to a number of 6 modes. In this work, it could be shown forthe first time that by using a DenseNet with 121 layers it is possible to breakthrough the hurdle of 6 modes. The advancement is demonstrated by a modedecomposition with 10 modes experimentally. The training process is based onsynthetic data. The proposed method is quantitatively compared to theconventional approach with digital holography. In addition, it is shown thatthe network can perform mode decomposition on a 55-mode fiber, which alsosupports modes unknown to the neural network. The smart detection using aDenseNet opens new ways for the application of multimode fibers in opticalcommunication networks for physical layer security.

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