Identification of multimodal vortex optical orbital angular momentum in multimode fiber speckle patterns.

Orbital angular momentum (OAM) multiplexing technology is a highly promising approach that can significantly increase the data capacity of optical communication systems. However, traditional optical communication systems are prone to atmospheric disturbances such as turbulence, which can degrade transmission quality. To mitigate this issue, multimode fibers (MMFs) have been introduced to reduce the impact of turbulence on signals. Moreover, previous studies have primarily focused on the identification of single vortex beams, facing challenges in accurately recognizing multiplexed modes. To address this challenge, this chapter proposes a multi-label image classification optimization algorithm based on transfer learning. By utilizing the pre-trained MobileNet V2 model as a feature extractor, this network structure can accurately identify 8-bit, 16-bit, and 24-bit multiplexed OAM from speckle patterns in multimode fibers, even with small sample datasets, achieving classification accuracies exceeding 95%. This method overcomes the limitations of traditional optical communication systems that are susceptible to atmospheric disturbances, providing new possibilities for long-distance transmission and increased data capacity in optical communication systems. • We use MMF to transmit vortex light, which significantly reduces the impact of atmospheric turbulence. • A multi-label image classification optimization algorithm based on transfer learning, ML-OAM-DemuxNet, is proposed to identify speckle patterns. • Cutting the center part of the speckle pattern for recognition can greatly reduce the computing cost of the network while maintaining the accuracy [ABSTRACT FROM AUTHOR]