  Non-linear effects in long-haul, high-speed optical fiber systems
significantly hinder channel capacity. While the Digital Backward Propagation
algorithm (DBP) with adaptive filter (ADF) can mitigate these effects, it
suffers from an overwhelming computational complexity. Recent solutions have
incorporated deep neural networks in a data-driven strategy to alleviate this
complexity in the DBP model. However, these models are often limited to a
specific symbol rate and channel number, necessitating retraining for different
settings, their performance declines significantly under high-speed and
high-power conditions. We introduce Meta-DSP, a novel data-driven nonlinear
compensation model based on meta-learning that processes multi-modal data
across diverse transmission rates, power levels, and channel numbers. This not
only enhances signal quality but also substantially reduces the complexity of
the nonlinear processing algorithm. Our model delivers a 0.7 dB increase in the
Q-factor over Electronic Dispersion Compensation (EDC), and compared to DBP, it
curtails computational complexity by a factor of ten while retaining comparable
performance. From the perspective of the entire signal processing system, the
core idea of Meta-DSP can be employed in any segment of the overall
communication system to enhance the model's scalability and generalization
performance. Our research substantiates Meta-DSP's proficiency in addressing
the critical parameters defining optical communication networks.
