Intensity-Only Mode Decomposition on Multimode Fibers Using a Densely Connected Convolutional Network

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 provides valuable knowledge about the communication channel. 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 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 is 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 training process is based on synthetic data. The advancement is demonstrated by a mode decomposition with 10 modes experimentally. A quantitative comparison of the proposed method with digital holography shows very good agreement. In addition, it is shown that the network can perform mode decomposition on a subset of 10 modes of 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.