Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications

Zekun Niu; Hang Yang; Lyu Li; Minghui Shi; Guozhi Xu; Weisheng Hu; Lilin Yi

doi:10.1038/s41377-024-01556-5

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Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications

Original Articles

- Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications
- Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications
- LSA 2024年13卷第9期页码：1931-1943
- Affiliations：
  
  State Key Lab of Advanced Optical Communication Systems and Networks, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
- Author bio：
  
  Lilin Yi (lilinyi@sjtu.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01556-5
  中图分类号：
- 收稿日期：2024-02-02，
  
  修回日期：2024-06-13，
  
  录用日期：2024-07-25，
  
  网络出版日期：2024-08-13，
  
  纸质出版日期：2024-09-30
- Accepted：
Scan QR Code
Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications[J]. LSA, 2024,13(9):1931-1943.

Niu, Z. K. et al. Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications. Light: Science & Applications, 13, 1931-1943 (2024).
Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications[J]. LSA, 2024,13(9):1931-1943. DOI： 10.1038/s41377-024-01556-5.

Niu, Z. K. et al. Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications. Light: Science & Applications, 13, 1931-1943 (2024). DOI： 10.1038/s41377-024-01556-5.

摘要

Abstract

The surge in interest regarding the next generation of optical fiber transmission has stimulated the development of digital signal processing (DSP) schemes that are highly cost-effective with both high performance and low complexity. As benchmarks for nonlinear compensation methods

however

traditional DSP designed with block-by-block modules for linear compensations

could exhibit residual linear effects after compensation

limiting the nonlinear compensation performance. Here we propose a high-efficient design thought for DSP based on the learnable perspectivity

called learnable DSP (LDSP). LDSP reuses the traditional DSP modules

regarding the whole DSP as a deep learning framework and optimizing the DSP parameters adaptively based on backpropagation algorithm from a global scale. This method not only establishes new standards in linear DSP performance but also serves as a critical benchmark for nonlinear DSP designs. In comparison to traditional DSP with hyperparameter optimization

a notable enhancement of approximately 1.21 dB in the Q factor for 400 Gb/s signal after 1600 km fiber transmission is experimentally demonstrated by combining LDSP and perturbation-based nonlinear compensation algorithm. Benefiting from the learnable model

LDSP can learn the best configuration adaptively with low complexity

reducing dependence on initial parameters. The proposed approach implements a symbol-rate DSP with a small bit error rate (BER) cost in exchange for a 48% complexity reduction compared to the conventional 2 samples/symbol processing. We believe that LDSP represents a new and highly efficient paradigm for DSP design

which is poised to attract considerable attention across various domains of optical communications.

关键词

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references

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