Transformer-based High-fidelity Modeling Method for Radio over Fiber Link

Abstract

High-fidelity modeling is of great importance for digital twin (DT) technology. A Transformer-based modeling method for radio over fiber (RoF) link is developed and experimentally demonstrated, in which the Transformer is a kind of deep learning network. The modeling performance of the constructed model by using the proposed method is evaluated, which is better than the ones based on fully connected neural network (FCNN), convolution neural network (CNN), and generative adversarial network (GAN). In addition, the modeling consistency with different laser launch power, fiber length, carrier frequency, and signal-to-noise ratio (SNR) is also investigated. Experimental results show that the fitting waveforms predicted by the proposed method agree well with the actual waveforms at the receiver end. The normalized mean square errors of the fitting waveforms are lower than the acceptable bound of 0.02.