Identifying structured light modes in a desert environment using machine learning algorithms

Ragheb, Amr; Saif, Waddah S.; Trichili, Abderrahmen; Ashry, Islam; Esmail, Maged Abdullah; Altamimi, Majid; Almaiman, Ahmed; Altubaishi, Essam Saleh; Ooi, Boon S.; Alouini, Mohamed‐Slim; Alshebeili, Saleh

doi:10.1364/oe.389210

Cited by 28 publications

(26 citation statements)

References 29 publications

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“…Recent works have focused on the beam structure for different orders of OAM after passing through diffuse media 7,8,13 . As a beam passes through diffuse media, the light is scattered the polarization becomes random and the beam's power decreases 14 .Studies of OAM propagation have treated transmission through multi scattering media for communication purposes, including transmission through atmosphere, desert environment and underwater 6,12,[15][16][17][18] . In such studies, transmittance was examined as a function of the topological charge, ℓ , representing the order of OAM.…”

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confidence: 99%

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OAM light propagation through tissue

Biton

Kupferman

Arnon

2021

Sci Rep

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A major challenge in use of the optical spectrum for communication and imaging applications is the scattering of light as it passes through diffuse media. Recent studies indicate that light beams with orbital angular momentum (OAM) can penetrate deeper through diffuse media than simple Gaussian beams. To the best knowledge of the authors, in this paper we describe for the first time an experiment examining transmission of OAM beams through biological tissue with thickness of up to a few centimeters, and for OAM modes reaching up to 20. Our results indicate that OAM beams do indeed show a higher transmittance relative to Gaussian beams, and that the greater the OAM, the higher the transmittance also up to 20, Our results extend measured results to highly multi scattering media and indicate that at 2.6 cm tissue thickness for OAM of order 20, we measure nearly 30% more power in comparison to a Gaussian beam. In addition, we develop a mathematical model describing the improved permeability. This work shows that OAM beams can be a valuable contribution to optical wireless communication (OWC) for medical implants, optical biological imaging, as well as recent innovative applications of medical diagnosis.

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confidence: 99%

“…Studies of OAM propagation have treated transmission through multi scattering media for communication purposes, including transmission through atmosphere, desert environment and underwater 6,12,[15][16][17][18] . In such studies, transmittance was examined as a function of the topological charge, ℓ , representing the order of OAM.…”

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confidence: 99%

OAM light propagation through tissue

Biton

Kupferman

Arnon

2021

Sci Rep

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“…Machine Learning (ML) algorithms have recently been successfully applied in various optical communication systems [120]. In the context of FSO communication, a wide range of ML algorithms have been proposed as a way to detect structured light beams after propagating through turbulent atmosphere [121]- [123]. Those algorithms allowed the identification of the structured light intensity profiles without the need to use mode sorters or mode demultiplexing devices [88].…”

Section: E Machine Learning Algorithmsmentioning

confidence: 99%

Roadmap to free space optics

et al. 2020

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With the ever-increasing demand for data and radio frequency spectrum becoming congested, Free Space Optics (FSO) may find a niche for situations where fiber is too expensive or too difficult to install. FSO is a cross-disciplinary field that draws from radio and fiber communication, astronomy, and even quantum optics, and it has seen major advances over the last three decades. In this tutorial-style review, we provide a broad overview of many of the important topics required to design, develop, and research the next generation of FSO technology.

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“…Indeed, ML algorithms based on the concept of end-to-end learning replace extracting hand-crafted features by iterating through deep learning architectures to automatically learn rich features directly from raw data; see Section II. For this reason, recently, ML-based algorithms have been used extensively in diverse fields of optical communication systems [20]- [24]. The use of ML techniques to perform OPM and MFI can provide many benefits either to the current optical networks or for the future adaptive and autonomous optical networks.…”

Section: A Advantages Of Using ML In Opm and Mfi For Optical Networkmentioning

confidence: 99%

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

Saif

Esmail

Ragheb

et al. 2020

IEEE Commun. Surv. Tutorials

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The trade-off between more user bandwidth and quality of service requirements introduces unprecedented challenges to the next generation smart optical networks. In this regard, the use of optical performance monitoring (OPM) and modulation format identification (MFI) techniques becomes a common need to enable the development of next-generation autonomous optical networks, with ultra-low latency and selfadaptability. Recently, machine learning (ML)-based techniques have emerged as a vital solution to many challenging aspects of OPM and MFI in terms of reliability, quality, and implementation efficiency. This paper surveys ML-based OPM and MFI techniques proposed in the literature. First, we address the key advantages of employing ML algorithms in optical networks. Then, we review the main optical impairments and modulation formats being monitored and classified, respectively, using ML algorithms. Additionally, we discuss the current status of optical networks in terms of MFI and OPM. This includes standards, monitoring parameters, and the available commercial products with their limitations. Second, we provide a comprehensive review of the available ML-based techniques for MFI, OPM, and joint MFI/OPM, describing their performance, advantages, and limitations. Third, we give an overview of the exiting ML-based OPM and MFI techniques for the emerging optical networks such as the new fiber-based networks that use future space division multiplexing techniques (e.g. few-mode fiber), the hybrid radioover-fiber networks, and the free space optical networks. Finally, we discuss the open issues, potential future research directions, and recommendations for the potential implementation of MLbased OPM and MFI techniques. Some lessons learned are presented after each section throughout the paper to help the reader identifying the gaps, weaknesses, and strengths in this field.

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Identifying structured light modes in a desert environment using machine learning algorithms

Cited by 28 publications

References 29 publications

OAM light propagation through tissue

OAM light propagation through tissue

Roadmap to free space optics

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

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