  The use of multimode fibers offers advantages in the field of communication
technology in terms of transferable information density and information
security. For applications using physical layer security or mode division
multiplexing, the complex transmission matrix must be known. To measure the
transmission matrix, the individual modes of the multimode fiber are excited
sequentially at the input and a mode decomposition is performed at the output.
Mode decomposition is usually performed using digital holography, which
requires the provision of a reference wave and leads to high efforts. To
overcome these drawbacks, a neural network is proposed, which performs mode
decomposition with intensity-only camera recordings of the multimode fiber
facet. Due to the high computational complexity of the problem, this approach
was usually limited to a number of 6 modes. In this work, it could be shown for
the first time that by using a DenseNet with 121 layers it is possible to break
through the hurdle of 6 modes. The advancement is demonstrated by a mode
decomposition with 10 modes experimentally. The training process is based on
synthetic data. The proposed method is quantitatively compared to the
conventional approach with digital holography. In addition, it is shown that
the network can perform mode decomposition on a 55-mode fiber, which also
supports modes unknown to the neural network. The smart detection using a
DenseNet opens new ways for the application of multimode fibers in optical
communication networks for physical layer security.
