In the wake of the gradual maturation of the theory and devices of the space division multiplexing optical transmission systems, the coding modulation of the next-generation space division multiplexing systems have to be updated as well. Compared with classic single-core single-mode fiber transmission systems, space division multiplexing optical transmission systems have multiplied receiver complexity, making code modulation with low receiver complexity desirable. In this paper, we build a seven-core optical fiber transmission system to experimentally verify the performance of differential amplitude phase shift keying (DAPSK) orthogonal frequency division multiplexing (OFDM). Through the joint modulation of DAPSK and OFDM, DAPSK-OFDM modulation does not require redundant data for channel compensation and phase noise compensation compared to quadrature amplitude modulation (QAM)-OFDM. The code division multiplexing modulation is deployed for the data transmitted by different fiber cores to form orthogonalized channel data transmission, which can effectively reduce the crosstalk between the cores. A 2 km short-distance sevencore optical transmission experimental system was successfully carried out to test the signal transmission performance of different fiber cores. Experimental results indicate that the DAPSK-OFDM signal is not sensitive to frequency offset, so it can be free of frequency offset estimation and channel equalization. In terms of transmission performance, the performance of 16-QAM-OFDM is better than that of 16-DAPSK-OFDM.
This paper proposed a novel chaotic physical security scheme based on Variational Auto-Encoder (VAE) for optical frequency division multiplexing-passive optical networks (OFDM-PON). We adopt the deep generative model VAE to generate chaotic sequences for the encryption of OFDM symbols. Different chaotic security schemes are included to improve the key space and sensitivity of chaotic models, thus enhancing the security of the OFDM-PON system. With the training materials of different chaotic security schemes, VAE can learn the complex structure of data distribution in various chaotic models and finally has the ability to generate the key group with a large space. Meanwhile, the benchmark performance of the OFDM system is experimentally investigated in terms of the bit error rate (BER). Moreover, owing to the parallel computing of GPU, the time consumed for training of VAE can be reduced to a large extent, and the time for generation of chaotic sequences via VAE is only 1.38% of that via repeated iteration of equations, which highlights the remarkable reduction in complexity of the chaotic physical security scheme.
An adversarial learning based knowledge distillation optical performance monitoring scheme based on has been proposed for 7 core fiber in this paper. Adversarial learning-based knowledge distillation simplified the architecture of the neural network for optical performance monitoring, including modulation format recognition (MFR) and optical signal-to-noise ratio (OSNR) estimation, in spatial division multiplexing (SDM) fiber transmission systems. On account of the knowledge distillation technologies, the knowledge in large teacher model is transferred to lightweight student model to reduce the complexity of the neural network and the difficulty of deployment. In addition, the adversarial learning is applied to the teacher-student architecture in order to enhance the generalization ability of the student model. After adversarial learning-based knowledge distillation, the student model is suitable for the deployment of the services in optical nodes. Experimentation results indicate that the student model has the 100 % modulation format recognition success rate for QPSK, 8QAM and 16QAM while the RMSE of optical signal-to-noise ratio (OSNR) estimation is below 0.1 dB. Due to its excellent performance and being easy to implement, the proposed scheme has the potential for the next-generation multiple core fiber based optical network.
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