Machine learning methods for optical communication systems and networks

Faisal Nadeem Khan, Qirui Fan, Chao Lu, Alan Pak Tao Lau

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

16 Citations (Scopus)

Abstract

Machine learning (ML) is being hailed as a new direction of innovation to transform future optical communication systems. Signal processing paradigms based on ML are being considered to solve certain critical problems in optical communications that cannot be easily tackled using conventional approaches. Recent applications of ML in various aspects of optical communications and networking such as nonlinear transmission systems, network planning and performance prediction, cross-layer network optimizations for software-defined networks, and autonomous and reliable network operations have shown promising results. However, to comprehend true potential of ML in optical communications, a basic understanding of the nature of ML concepts is indispensable. In this chapter we describe mathematical foundations of several key ML methods from communication theory and signal processing perspectives and highlight the types of problems in optical communications and networking where they can be useful. We also provide an overview of existing ML applications in optical communication systems with an emphasis on physical layer.

Original languageEnglish
Title of host publicationOptical Fiber Telecommunications VII
PublisherElsevier
Pages921-978
Number of pages58
ISBN (Electronic)9780128165027
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Deep learning
  • Fiber-optic communications
  • Machine learning
  • Software-defined optical networks

ASJC Scopus subject areas

  • Engineering(all)

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