Transformer-Based Long Distance Fiber Channel Modeling for Optical OFDM Systems

The fiber channel model plays an essential role in the simulation and design of optical fiber communication systems. However, it is difficult for conventional model-driven modeling to balance accuracy and efficiency, especially in optical orthogonal frequency division multiplexing (OFDM) systems with complex and long-haul transmission. We introduce the simplified Transformer into optical OFDM systems and combine it with the feature decoupled distributed (FDD) scheme for fast and accurate fiber channel modeling. Unlike the popular Transformer architectures, we remove the Decoder part and cancel the self-attention with quadratic complexity, significantly reducing the computational cost. The modeling performance is investigated from the nonlinear fitting capability, accuracy, and generalization ability. The transmission distance ranges from 80 km to 1600 km. The highly matched four-wave mixing (FWM) power, low error vector magnitudes (EVMs), and similar signal-noise ratios (SNRs) demonstrate the high precision and robustness of the model. Furthermore, the modeling is studied under different transmission rates and is proved to be reliable over a wide transmission bandwidth. Compared to the bidirectional long short-term memory (Bi-LSTM), the Transformer performs better in accuracy and has lower computational and memory costs. For modeling under the same conditions, the required running time of the Transformer is about 60% of Bi-LSTM, less than 1% of the split-step Fourier method (SSFM). The Transformer-based method achieves high precision modeling of the fiber channel in the long-distance and high-rate optical OFDM system and makes a significant breakthrough in complexity.