Fiber Transmission Model with Parameterized Inputs based on GPT-PINN Neural Network
This work addresses computational efficiency and physical interpretability in fiber optics modeling, though it appears incremental by building on existing principle-driven models.
The paper tackles fiber transmission modeling for short-distance communication by developing a parameterized input model using GPT-PINN neural networks, enabling universal solutions for bit rates from 2Gbps to 50Gbps without retraining.
In this manuscript, a novelty principle driven fiber transmission model for short-distance transmission with parameterized inputs is put forward. By taking into the account of the previously proposed principle driven fiber model, the reduced basis expansion method and transforming the parameterized inputs into parameterized coefficients of the Nonlinear Schrodinger Equations, universal solutions with respect to inputs corresponding to different bit rates can all be obtained without the need of re-training the whole model. This model, once adopted, can have prominent advantages in both computation efficiency and physical background. Besides, this model can still be effectively trained without the needs of transmitted signals collected in advance. Tasks of on-off keying signals with bit rates ranging from 2Gbps to 50Gbps are adopted to demonstrate the fidelity of the model.