Introduction to machine learning techniques: An optical communication's perspective

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

1 Citation (Scopus)


Machine learning (ML) has revolutionized a number of science and engineering disciplines over the past few years. It is also being considered as a new direction of innovation to transform future fiber-optic communication systems. Recently, there has been an increasing amount of research in both industry and academia to embed and benefit from ML-based frameworks in various aspects of optical communications and networking and state-of-the-art results have already been achieved in many cases. However, in order to fathom real potential of ML in fiber-optic communication systems, it is imperative to have a basic understanding of fundamental ML concepts. In this chapter, we will describe the reasons behind recent popularity of ML paradigm in optical networks and why/where/how it can play a decisive role. We will discuss mathematical foundations of several key conventional ML techniques as well as modern deep learning (DL) methods from communication theory and signal processing perspectives and identify the kind of problems in optical communications and networking where they can be particularly helpful. The future role of ML as an enabling technology for next-generation intelligent and autonomous software-defined optical networks will be highlighted. A brief discussion on ML tools along with some useful links for online resources will also be provided for the sake of completion.

Original languageEnglish
Title of host publicationMachine Learning for Future Fiber-Optic Communication Systems
Number of pages42
ISBN (Electronic)9780323852272
ISBN (Print)9780323852289
Publication statusPublished - 1 Jan 2022


  • Artificial intelligence
  • Autonomous networks
  • Deep learning
  • Machine learning
  • Network intelligence

ASJC Scopus subject areas

  • General Engineering
  • General Physics and Astronomy


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