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Egor S Manuylovich1,2, Vladislav V Dvoyrin3,4, Sergei K Turitsyn3,4

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Area of Science:

  • Optics and Photonics
  • Signal Processing
  • Fiber Communications

Background:

  • Optical phase information retrieval from intensity measurements is crucial for cost-efficient devices.
  • Few-mode fibers (FMFs) offer potential for advanced applications, but require spatial mode structure knowledge for phase recovery.
  • Current mode decomposition algorithms (optimization, neural networks) suffer from high computational costs and latency, hindering real-time applications.

Purpose of the Study:

  • To present a high-performance, computationally efficient mode decomposition algorithm for FMFs.
  • To overcome the speed limitations of existing phase retrieval techniques.
  • To enable low-cost phase retrieval receivers for FMF applications.

Main Methods:

  • Developed a novel mathematical algorithm for mode decomposition based on output intensity distribution.
  • The algorithm does not employ machine learning techniques.
  • Algorithm performance was evaluated based on processing time.

Main Results:

  • Achieved a processing time in the tens of microseconds range.
  • The proposed algorithm is orders of magnitude faster than state-of-the-art deep-learning methods.
  • Demonstrated a significant improvement in signal processing speed for optical phase retrieval.

Conclusions:

  • The developed algorithm offers a high-performance, low-latency solution for optical phase retrieval in FMFs.
  • This non-machine learning approach can stimulate research beyond current deep-learning methods.
  • Results pave the way for low-cost phase retrieval receivers in telecommunications and imaging.