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.

Multimode fibers (MMF) are promising candidates to increase the data rate while reducing the space required for optical fiber networks. However, their use is hampered by mode mixing and other effects, leading to speckled output patterns. This can be overcome by measuring the transmission matrix (TM) of a multimode fiber. In this contribution, a mode-selective excitation of complex amplitudes is performed with only one phase-only spatial light modulator. The light field propagating through the fiber is measured holographically and is analyzed by a rapid decomposition method. This technique requires a small amount of measurements N, which corresponds to the degree of freedom of the fiber. The TM determines the amplitude and phase relationships of the modes, which allows us to understand the mode scrambling processes in the MMF and can be used for mode division multiplexing.

Multimode fibers are regarded as the key technology for the steady increase in data rates in optical communication. However, light propagation in multimode fibers is complex and can lead to distortions in the transmission of information. Therefore, strategies to control the propagation of light should be developed. These strategies include the measurement of the amplitude and phase of the light field after propagation through the fiber. This is usually done with holographic approaches. In this paper, we discuss the use of a deep neural network to determine the amplitude and phase information from simple intensity-only camera images. A new type of training was developed, which is much more robust and precise than conventional training data designs. We show that the performance of the deep neural network is comparable to digital holography, but requires significantly smaller efforts. The fast characterization of multimode fibers is particularly suitable for high-performance applications like cyberphysical systems in the internet of things.

High-power fiber lasers are powerful tools used in science, industry, and defense. A major roadblock for further power scaling of single-frequency fiber laser amplifiers is stimulated Brillouin scattering. Efforts have been made to mitigate this nonlinear process, but these were mostly limited to single-mode or few-mode fiber amplifiers, which have good beam quality. Here, we explored a highly multimode fiber amplifier in which stimulated Brillouin scattering was greatly suppressed due to a reduction of light intensity in a large fiber core and a broadening of the Brillouin scattering spectrum by multimode excitation. By applying a spatial wavefront shaping technique to the input light of a nonlinear amplifier, the output beam was focused to a diffraction-limited spot. Our multimode fiber amplifier can operate at high power with high efficiency and narrow linewidth, which ensures high coherence. Optical wavefront shaping enables coherent control of multimode laser amplification, with potential applications in coherent beam combining, large-scale interferometry and directed energy delivery.

Wavefront shaping with spatial light modulators (SLMs) enables aberration correction, especially for light control through complex media, like biological tissues and multimode fibres. High-fidelity light field shaping is associated with the calculation of computer generated holograms (CGHs), of which there are a variety of algorithms. The achievable performance of CGH algorithms depends on various parameters. In this paper, four different algorithms for CGHs are presented and compared for complex light field generation. Two iterative, double constraint Gerchberg-Saxton and direct search, and the two analytical, superpixel and phase encoding, algorithms are investigated. For each algorithm, a parameter study is performed varying the modulator’s pixel number and phase resolution. The analysis refers to mode field generation in multimode fibre endoscopes and communication. This enables generality by generating specific mode combinations according to certain spatial frequency power spectra. Thus, the algorithms are compared varying spatial frequencies applied to different implementation scenarios. Our results demonstrate that the choice of algorithms has a significant impact on the achievable performance. This comprehensive study provides the required guide for CGH algorithm selection, improving holographic systems towards multimode fibre endoscopy and communications.

Multi- and few-mode fibers (FMFs) promise to enhance the capacity of optical communication networks by orders of magnitude. The key for this evolution was the strong advancement of computational approaches that allowed inherent complex light transmission to be surpassed, learned, or controlled, reined in by modal crosstalk and mode-dependent losses. However, complex light transmission through FMFs can be learned by a single hidden layer neural network (NN). The emerging developments in NNs additionally allow the implementation of novel concepts for security enhancements in optical communication. Once the transmission characteristics of FMFs are learned, it is possible to survey the incoming and outgoing light fields via monitoring channels during data transmission. If an eavesdropper tries to gain unauthorized access to the FMF, its transmission properties are impaired through sensitive modal crosstalk. This process is registered by the NN and thus the eavesdropper is revealed. With our solution, the security of optical communication can be improved.