Dispersion and nonlinear compensation in a 32 × 200  Gb/s phase conjugated twin waves dense wavelength division multiplexed system

Neural networks (NNs), random forests (RF), and extreme gradient boosting (XGBoost) are applied separately to inversely design the ring-core few-mode fiber (FMF) with a desired weakly coupled performance. We demonstrate the procedure of inverse designing of FMF via machine learning (ML) algorithms and evaluate the prediction accuracy of the above ML algorithms. Compared with RF and XGBoost, the NN performs the highest prediction accuracy. For the NN, RF, and XGBoost, the correlation coefficients of the mode effective index difference are 0.99993, 0.99857, and 0.99937, respectively. Subsequently, by utilizing the method of permuting feature importance ranking, we obtain the high-correlation fiber structural features with the mode effective index difference. Moreover, we analyze the effect of the minimum index difference between two adjacent modes ( Δ n e f f , m i n ) on the structural parameters and get consistent feature attribution via permuting feature importance ranking above. Finally, we design a weakly coupled ring-core fiber that supports four modes ( H E 11 , T E 01 , H E 21 , T M 01 ) based on the NN algorithm, which could be successfully applied in vector mode division multiplexing.

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Few-mode fiber design for multiple-input-multiple-output-less mode division multiplexing by machine learning

Cheng

Guo

Cao

et al. 2022

J. Opt. Soc. Am. B

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Forward design method for the design of panda polarization-maintaining few-mode optical fiber based on artificial neural network

Hu,

Li,

Chen

et al. 2024

Opt. Express

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Panda polarization-maintaining few-mode optical fiber (PPMFMOF) has important research significance in the short distance optical transmission field owing to its advantages of weak nonlinear effects, which is benefit to reduce the use of digital signal processing equipment. Designing a high-performance PPMFMOF quickly and efficiently is expected and yet challenging. In this article, we demonstrated a forward design method for the design of PPMFMOF based on artificial neural network (ANN) to solve the problems of inefficient and time-consuming PPMFMOF design in traditional design method. By studying the influence of different ANN models on the fiber performance, the approximate range of the optimal value was obtained in advance, then the minimum effective refractive index difference (Δneff,min) between adjacent LP modes was used as the optimization object, finally design of PPMFMOF supporting 10 LP modes in C + L band was successfully realized. This method provided low time-consuming, high-efficiency and high-accuracy for the fast design of PPMFMOF and the maximum mean absolute percentage error (MAPE) of the ANN model to predict the effective refractive index (neff) of 10 LP modes is only 3.2211 × 10−7. We believe that the proposed method could also be quickly and accurately applied to other functional optical fiber designs.

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Dispersion and nonlinear compensation in a 32 × 200 Gb/s phase conjugated twin waves dense wavelength division multiplexed system

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Few-mode fiber design for multiple-input-multiple-output-less mode division multiplexing by machine learning

Few-mode fiber design for multiple-input-multiple-output-less mode division multiplexing by machine learning

Forward design method for the design of panda polarization-maintaining few-mode optical fiber based on artificial neural network

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