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Non-technological barriers: the last frontier towards AI-powered intelligent optical networks

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  • Faisal Nadeem Khan

    (Tsinghua University
    Tsinghua University)

Abstract

Machine learning (ML) has been remarkably successful in transforming numerous scientific and technological fields in recent years including computer vision, natural language processing, speech recognition, bioinformatics, etc. Naturally, it has long been considered as a promising mechanism to fundamentally revolutionize the existing archaic optical networks into next-generation smart and autonomous entities. However, despite its promise and extensive research conducted over the last decade, the ML paradigm has so far not been triumphant in achieving widespread adoption in commercial optical networks. In our perspective, this is primarily due to non-addressal of a number of critical non-technological issues surrounding ML-based solutions’ development and use in real-world optical networks. The vision of intelligent and autonomous fiber-optic networks, powered by ML, will always remain a distant dream until these so far neglected factors are openly confronted by all relevant stakeholders and categorically resolved.

Suggested Citation

  • Faisal Nadeem Khan, 2024. "Non-technological barriers: the last frontier towards AI-powered intelligent optical networks," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50307-y
    DOI: 10.1038/s41467-024-50307-y
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    1. Shaoliang Zhang & Fatih Yaman & Kohei Nakamura & Takanori Inoue & Valey Kamalov & Ljupcho Jovanovski & Vijay Vusirikala & Eduardo Mateo & Yoshihisa Inada & Ting Wang, 2019. "Field and lab experimental demonstration of nonlinear impairment compensation using neural networks," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    2. Qirui Fan & Gai Zhou & Tao Gui & Chao Lu & Alan Pak Tao Lau, 2020. "Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learning," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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