Digital twin (DT) modeling is essential to optical fiber communication systems, particularly for enhancing system performance, controlling the system in real time, and understanding signal nonlinearity. Conventional s...
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Digital twin (DT) modeling is essential to optical fiber communication systems, particularly for enhancing system performance, controlling the system in real time, and understanding signal nonlinearity. Conventional split-step Fourier method -based simulations, however, struggle with wide-band transmissions, plagued by increasing complexity and inherent biases due to inconsistent link parameter availability. Addressing these challenges, a hybrid data-driven and model-driven DT approach for the wide-band and long-haul physical systems with various system effects is developed. The approach utilizes a neural network (NN) to capture fiber nonlinear features as well as biased perturbations as "lumped" stochastic noises while offloading the linear effects to modules described by physical models of link elements. The model, tested in a 30.5-Tbps 1200 km fiber transmission link with 40 channels, achieves a mean Q factor error of less than 0.1 dB and a maximum runtime of 1.3 s for NN processing under various launch powers, transmission lengths, and optical signal-to-noise ratios. Furthermore, the study has implemented a nonlinear compensation algorithm on the DT model, yielding a consistent enhancement in experimental data. The accuracy and adaptability of the DT model underline its suitability for planning, design, and optimization within the physical optical fiber communication systems. Digital twin (DT) is crucial in optical fiber communication. A novel DT incorporates neural networks to model nonlinearities and stochastic noises, while linearities are handled by physical models. This model, validated on a 30.5-Tbps, 1200 km link with 40 channels, achieves a Q factor error of less than 0.1 dB and a maximum runtime of 1.3 s, enhancing system design and optimization. image
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