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Physics-informed neural Volterra compensation enabling over 2600× efficiency improvement in 12,057-km ultra-long-haul coherent transmission.

Overview

Authors: Xingchen He1, Lianshan Yan1, Lin Jiang1, Anlin Yi1, Wei Pan1, Bin Luo1, Zhengyu Pu1, Alan Pak Tao Lau2, Changyuan Yu2
ORCID iDs: Xingchen He, Lianshan Yan, Lin Jiang, Changyuan Yu
  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China
  2. Photonics Research Institute, Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong China
Institutions: Southwest Jiaotong University (China); Hong Kong Polytechnic University (Hong Kong SAR China)
Journal: Communications engineering, volume 5, issue 1, article 153
Dates: received 9 December 2025; accepted 22 May 2026; published online 3 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s44172-026-00696-3 · PMID 42237027 · PMCID PMC13542127 · OpenAlex W7163374825
Open access: gold, a free copy (OpenAlex)
Status: code on request
Methods: Spectral & time-frequency
Keywords: Fibre optics and optical communications, Computational science, Nonlinear optics
Topic: Optical Network Technologies (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (62571458, 62431024, 62575248)
Citations: not cited yet (Europe PMC); 46 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s44172-026-00696-3.

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Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to a dataset: github.com/remifan/labptptm2
  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s44172-026-00696-3.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 3 keywords, 1 funder, 15 references.

Cite

This paper

He, X., Yan, L., Jiang, L., Yi, A., Pan, W., Luo, B., Pu, Z., Lau, A. P. T., & Yu, C. (2026). Physics-informed neural Volterra compensation enabling over 2600× efficiency improvement in 12,057-km ultra-long-haul coherent transmission. Communications engineering, 5(1), 153. https://doi.org/10.1038/s44172-026-00696-3

BibTeX

@article{he2026physics,
author = {He, Xingchen and Yan, Lianshan and Jiang, Lin and Yi, Anlin and Pan, Wei and Luo, Bin and Pu, Zhengyu and Lau, Alan Pak Tao and Yu, Changyuan},
title = {{Physics-informed neural Volterra compensation enabling over 2600× efficiency improvement in 12,057-km ultra-long-haul coherent transmission}},
journal = {Communications engineering},
year = {2026},
month = jun,
volume = {5},
number = {1},
pages = {153},
publisher = {Nature Publishing Group},
issn = {2731-3395},
doi = {10.1038/s44172-026-00696-3},
url = {https://doi.org/10.1038/s44172-026-00696-3},
pmid = {42237027},
pmcid = {PMC13542127}
}

RIS

TY - JOUR
AU - He, Xingchen
AU - Yan, Lianshan
AU - Jiang, Lin
AU - Yi, Anlin
AU - Pan, Wei
AU - Luo, Bin
AU - Pu, Zhengyu
AU - Lau, Alan Pak Tao
AU - Yu, Changyuan
TI - Physics-informed neural Volterra compensation enabling over 2600× efficiency improvement in 12,057-km ultra-long-haul coherent transmission
T2 - Communications engineering
J2 - Commun Eng
PY - 2026
DA - 2026/06/03
VL - 5
IS - 1
SP - 153
SN - 2731-3395
PB - Nature Publishing Group
DO - 10.1038/s44172-026-00696-3
UR - https://doi.org/10.1038/s44172-026-00696-3
LA - en
ER -

CSL-JSON

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