Photonics (Aug 2023)

Machine Learning for Self-Coherent Detection Short-Reach Optical Communications

  • Qi Wu,
  • Zhaopeng Xu,
  • Yixiao Zhu,
  • Yikun Zhang,
  • Honglin Ji,
  • Yu Yang,
  • Gang Qiao,
  • Lulu Liu,
  • Shangcheng Wang,
  • Junpeng Liang,
  • Jinlong Wei,
  • Jiali Li,
  • Zhixue He,
  • Qunbi Zhuge,
  • Weisheng Hu

DOI
https://doi.org/10.3390/photonics10091001
Journal volume & issue
Vol. 10, no. 9
p. 1001

Abstract

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Driven by emerging technologies such as the Internet of Things, 4K/8K video applications, virtual reality, and the metaverse, global internet protocol traffic has experienced an explosive growth in recent years. The surge in traffic imposes higher requirements for the data rate, spectral efficiency, cost, and power consumption of optical transceivers in short-reach optical networks, including data-center interconnects, passive optical networks, and 5G front-haul networks. Recently, a number of self-coherent detection (SCD) systems have been proposed and gained considerable attention due to their spectral efficiency and low cost. Compared with coherent detection, the narrow-linewidth and high-stable local oscillator can be saved at the receiver, significantly reducing the hardware complexity and cost of optical modules. At the same time, machine learning (ML) algorithms have demonstrated a remarkable performance in various types of optical communication applications, including channel equalization, constellation optimization, and optical performance monitoring. ML can also find its place in SCD systems in these scenarios. In this paper, we provide a comprehensive review of the recent progress in SCD systems designed for high-speed optical short- to medium-reach transmission links. We discuss the diverse applications and the future perspectives of ML for these SCD systems.

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