Deep learning as a highly efficient tool for digital signal processing design

Andrey Pryamikov

doi:10.1038/s41377-024-01599-8

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Deep learning as a highly efficient tool for digital signal processing design

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- Deep learning as a highly efficient tool for digital signal processing design
- Light: Science & Applications Vol. 13, Issue 11, Pages: 2434-2435(2024)
- Affiliations：
  
  Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia
- Author bio：
  
  Andrey Pryamikov (pryamikov@mail.ru)
- Funds：
- DOI：10.1038/s41377-024-01599-8
  CLC：
- Published：30 November 2024，
  
  Published Online：11 September 2024，
- Accepted：
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Pryamikov, A. Deep learning as a highly efficient tool for digital signal processing design. Light: Science & Applications, 13, 2434-2435 (2024).
DOI：

Pryamikov, A. Deep learning as a highly efficient tool for digital signal processing design. Light: Science & Applications, 13, 2434-2435 (2024). DOI： 10.1038/s41377-024-01599-8.

摘要

Abstract

The backpropagation algorithm

the most widely used algorithm for training artificial neural networks

can be effectively applied to the development of digital signal processing schemes in the optical fiber transmission systems. Digital signal processing as a deep learning framework can lead to a new highly efficient paradigm for cost-effective digital signal processing designes with low complexity.

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references

Schvan, P. et al. A 24GS/s 6b ADC in 90 nm CMOS. InProc. IEEE International Solid-State Circuits Conference-Digest of Technical Papers. 544–634(IEEE, 2008).

Savory, S. J. Digital filters for coherent optical receivers.Opt. Express16, 804–817 (2008)..

Wang, H. D. et al. Fast-convergence digital signal processing for coherent PON using digital SCM.J. Lightwave Technol.41, 4635–4643 (2023)..

Vassilieva, O., Kim, I.&Ikeuchi, T. Enabling technologies for fiber nonlinearity mitigation in high capacity transmission systems.J. Lightwave Technol.37, 50–60 (2019)..

Nevin, J. W. et al. Machine learning for optical fiber communication systems: an introduction and overview.APL Photonics6, 121101 (2021)..

Freire, P. et al. Artificial neural networks for photonic applications—from algorithms to implementation: tutorial.Adv. Opt. Photonics15, 739–834 (2023)..

Fan, Q. R., Lu, C.&Lau, A. P. T. Combined neural network and adaptive DSP training for long-haul optical communications.J. Lightwave Technol.39, 7083–7091 (2021)..

Niu, Z. K. et al. Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications.Light Sci. Appl.13, 188 (2024)..

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