Low-Complexity Fiber Nonlinearity Compensation Based on Operator Learning Over 12 075 km Single Model Fiber

Fiber nonlinearity is a significant constraint on the maximum achievable capacity in long-distance fiber optic transmission systems. The fiber nonlinearity compensation can be done in digital signal processing through the inversion of the Manakov model using the split-step Fourier method (SSFM) with appropriate inverted channel parameters, which are implemented via digital backpropagation (DBP) algorithm. Nevertheless, the practical deployment of this algorithm and its variants require a significant amount of latency and signal processing resources. Currently, the data-driven approximation of partial differential equations (PDEs) is rapidly emerging as a powerful paradigm, wherein the operator learning framework has been demonstrated to effectively approximate the solution space of PDEs. Here, we construct an inverse parameterized Manakov model based on the neural operator, utilizing it to compensate the fiber nonlinearity impairments at low computational complexity. The model proposed has been validated in a 12,075 km wavelength division multiplexing (WDM) system. The received signal waveform compensated by this model produces results that are similar to single-channel DBP (optimal step size), with an MSE of 0.0008. The analysis of the computational complexity shows that the proposed algorithm achieves a performance loss of 0.024 dB compared to single-channel DBP under the optimal step size, but its highly parallel nature significantly reduces computational latency.