1. Reducing Training Time of Deep Learning Based Digital Backpropagation by Stacking
- Author
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Bertold Ian Bitachon, Marco Eppenberger, Benedikt Baeuerle, and Juerg Leuthold
- Subjects
Electrical and Electronic Engineering ,Atomic and Molecular Physics, and Optics ,Electronic, Optical and Magnetic Materials - Abstract
A method for reducing the training time of a deep learning based digital backpropagation (DL-DBP) is presented. The method is based on dividing a link into smaller sections. A smaller section is then compensated by the DL-DBP algorithm and the same trained model is then reapplied to the subsequent sections. We show in a 32 GBd 16QAM 2400 km 5-channel wavelength division multiplexing transmission link experiment that the proposed stacked DL-DBPs provides a 0.41 dB gain with respect to linear compensation scheme. This needs to be compared with a 0.56 dB gain achieved by a non-stacked DL-DBPs compensated scheme for the price of a 203% increase in total training time. Furthermore, it is shown that by only training the last section of the stacked DL-DBP, one can increase the compensation performance to 0.48 dB., IEEE Photonics Technology Letters, 34 (7), ISSN:1041-1135, ISSN:1941-0174
- Published
- 2022