Reducing Training Time of Deep Learning Based Digital Backpropagation by Stacking
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Date
2022-04-01
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Journal Article
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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.
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published
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34 (7)
Pages / Article No.
387 - 390
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IEEE
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03974 - Leuthold, Juerg / Leuthold, Juerg
02635 - Institut für Elektromagnetische Felder / Institute of Electromagnetic Fields