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

TL;DR

This paper introduces a DenseNet-based neural network that performs intensity-only mode decomposition on multimode fibers, enabling analysis of more modes than previous methods and supporting applications in secure optical communications.

Contribution

The study demonstrates that a 121-layer DenseNet can decompose 10 modes experimentally and handle 55 modes, surpassing the previous 6-mode limit using intensity-only data.

Findings

Successfully decomposed 10 modes experimentally.

Supported mode decomposition on a 55-mode fiber.

Outperformed traditional digital holography methods.

Abstract

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.