AI Article Synopsis
- The paper introduces a new data-driven fiber model that uses a deep neural network with multi-head attention to predict signal evolution in optical fiber telecommunications.
- This model offers faster computation times while maintaining accuracy, outperforming traditional methods like the split-step Fourier method (SSFM).
- It effectively balances prediction accuracy and distance generalization, successfully predicting high bit rate (16-QAM 160Gbps) signals over distances of 0 to 100 km, with or without noise.
Article Abstract
In this paper, we put forward a data-driven fiber model based on the deep neural network with multi-head attention mechanism. This model, which predicts signal evolution through fiber transmission in optical fiber telecommunications, can have advantages in computation time without losing much accuracy compared with conventional split-step fourier method (SSFM). In contrast with other neural network based models, this model obtains a relatively good balance between prediction accuracy and distance generalization especially in cases where higher bit rate and more complicated modulation formats are adopted. By numerically demonstration, this model can have ability of predicting up to 16-QAM 160Gbps signals with any transmission distances ranging from 0 to 100 km under both circumstances of the signals without or with the noise.
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| http://dx.doi.org/10.1364/OE.472981 | DOI Listing |
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