Efficient channel modeling of structured light in turbulence using generative adversarial networks

Briantcev, Dmitrii; Cox, Mitchell A.; Trichili, Abderrahmen; Drozdov, Alice; Ooi, Boon S.; Alouini, Mohamed‐Slim

doi:10.1364/oe.448899

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…Some ML algorithms, such as generative adversarial networks (GANs), can also help augment the data obtained from channel measurements needed for data-driven modeling. We note that GANs are machine learning-based frameworks that learn to generate new data with the same statistics as their training sets [170], [171]. The benefits of these algorithms may cover simulation scenarios beyond what can be obtained with theoretical modeling and experimental measurements, particularly if we want to utilize signals in the optical band and potentially in the THz band.…”

Section: E Harnessing the Power Of Machine Learning For Maritime Comm...mentioning

confidence: 99%

Maritime Communications: A Survey on Enabling Technologies, Opportunities, and Challenges

Alqurashi

Trichili

Saeed

et al. 2023

IEEE Internet Things J.

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Water covers 71% of the Earth's surface, where the steady increase in oceanic activities has promoted the need for reliable maritime communication technologies. The existing maritime communication systems involve terrestrial, aerial, and space networks. This paper presents a holistic overview of the different forms of maritime communications and provides the latest advances in various marine technologies. The paper first introduces the different techniques used for maritime communications over the radio frequency (RF) and optical bands. Then, we present the channel models for RF and optical bands, modulation and coding schemes, coverage and capacity, and radio resource management in maritime communications. After that, the paper presents some emerging use cases of maritime networks, such as the Internet of Ships and the ship-to-underwater Internet of things. Finally, we highlight a few exciting open challenges and identify a set of future research directions for maritime communication, including bringing broadband connectivity to the deep sea, using terahertz and visible light signals for on-board applications, and data-driven modeling for radio and optical marine propagation.

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Section: E Harnessing the Power Of Machine Learning For Maritime Comm...mentioning

confidence: 99%

Maritime Communications: A Survey on Enabling Technologies, Opportunities, and Challenges

Alqurashi

Trichili

Saeed

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…The rapid development of deep learning in recent years has inspired the researchers in optics with a new perspective [20]. Neural network architectures not only have shown many advantages in turbulence compensation [21] and turbulence channel modeling [22], but can also be naturally adopted for the task of OAM recognition. This idea has been implemented by some authors.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Recognition of Orbital Angular Momentum Modes Over Atmospheric Turbulence Channels Assisted by Vortex Phase Modulation

Xiang

Zeng

et al. 2022

IEEE Photonics J.

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Accurate recognition of orbital angular momentum (OAM) modes is a major challenge for OAM-based optical communications over atmospheric turbulence channels. The turbulence-induced distortions cause difficulties for the receiver to distinguish between adjacent OAM modes. Deep learning, such as convolutional neural networks (CNN), has been a promising technique in solving this problem. To improve the recognition performance, we propose a vortex modulation method that can magnify the subtle differences between closely adjacent OAM states. This allows the CNN to capture the image features more effectively and to recognize the topological charges more accurately. Numerical results show high recognition accuracy for both integer topological charges and fractional ones even under strong turbulence intensity and long propagation distance, which demonstrate the utility of the proposed method.

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GAN-Based Data Augmentation for AI-Enabled ATP in Free Space Optical Communication

Liu,

Song

et al. 2024

IEEE Commun. Lett.

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Efficient channel modeling of structured light in turbulence using generative adversarial networks

Cited by 6 publications

References 27 publications

Maritime Communications: A Survey on Enabling Technologies, Opportunities, and Challenges

Maritime Communications: A Survey on Enabling Technologies, Opportunities, and Challenges

Deep Learning Recognition of Orbital Angular Momentum Modes Over Atmospheric Turbulence Channels Assisted by Vortex Phase Modulation

GAN-Based Data Augmentation for AI-Enabled ATP in Free Space Optical Communication

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