Abstract
High-symbol-rate coherent optical transceivers suffer more from the critical responses of transceiver components at high frequency, especially when applying a higher order modulation format. Recently, we proposed in [20] a neural network (NN)-based digital pre-distortion (DPD) technique trained to mitigate the transceiver response of a 128~GBaud optical coherent transmission system. In this paper, we further detail this work and assess the NN-based DPD by training it using either a direct learning architecture (DLA) or an indirect learning architecture (ILA), and compare performance against a Volterra series-based DPD and a linear DPD. Furthermore, we willfully increase the transmitter nonlinearity and compare the performance of the three DPDs considered. The proposed NN-based DPD trained using DLA performs the best among the three contenders, providing more than 1~dB signal-to-noise ratio (SNR) gains for uniform 64-quadrature amplitude modulation (QAM) and PCS-256-QAM signals at the output of a conventional coherent receiver DSP. Finally, the NN-based DPD enables achieving a record 1.61~Tb/s net rate transmission on a single channel after 80~km of standard single mode fiber (SSMF).
Original language | English |
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Pages (from-to) | 597-606 |
Journal | Journal of Lightwave Technology |
Volume | 40 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Artificial neural networks
- digital pre-distortion
- digital signal processing
- machine learning and optical fiber communication
- Nonlinear optics
- Optical amplifiers
- Optical fiber amplifiers
- Optical fibers
- Optical modulation
- Optical transmitters