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Updated: Jun 5, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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Photonic neuromorphic technologies in optical communications.

Apostolos Argyris1

  • 1Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, 07122, Spain.

Nanophotonics (Berlin, Germany)
|December 5, 2024
PubMed
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Machine learning and neuromorphic computing enhance optical communications with advanced signal processing. Photonic reservoir computing offers revolutionary solutions for faster, more complex data recovery and network monitoring.

Area of Science:

  • Photonics and Optical Communications
  • Artificial Intelligence
  • Computer Engineering

Background:

  • Optical communications require high computational speed and complexity for advanced signal processing.
  • Digital signal processing (DSP) techniques are crucial for data recovery, transmission reach, and network integrity.
  • Machine learning (ML) and neural networks (NNs) offer new approaches to signal processing challenges.

Purpose of the Study:

  • To review established DSP techniques and novel ML/NN approaches in optical communications.
  • To explore neuromorphic computing proposals for photonic hardware.
  • To discuss the application of photonic reservoir computing in optical signal processing.

Main Methods:

  • Review of established digital signal processing (DSP) techniques.
Keywords:
fiber transmissionmachine learningneuromorphic computingoptical communicationsphotonic systemsreservoir computing

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  • Exploration of machine learning (ML) and neural network (NN) architectures.
  • Analysis of photonic implementations of reservoir computing (RC).
  • Discussion of feed-forward and recurrent network topologies in photonics.

    • Main Results:

    • Neuromorphic computing applied to photonic hardware offers new perspectives for optical signal processing.
    • Photonic reservoir computing implementations show promise for revolutionizing the field.
    • Photonic topologies have been tested for channel equalization, optical header recognition, and data recovery.

    Conclusions:

    • Machine learning and neuromorphic computing, particularly through photonic reservoir computing, are vital for advancing optical communications.
    • These approaches provide innovative solutions for complex signal processing tasks.
    • Future research directions include further development and application of photonic neuromorphic systems.