Machine Learning Framework Combining Radial Phase Grating and Channel Information-Assisted Underwater Wireless Optical OAM Communications

Wang, Wei; Wang, Ping; Song, Yatong; Pang, Weina; Li, Shuang; Nie, Yuanhao

doi:10.1109/lcomm.2021.3105657

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Convolutional and fully connected layers are employed to analyze the data. The proposed transfer learning (TL) based ML framework exploits image features and channel information, and fine-tuning is utilized to improve network performance [98].…”

Section: ML In Non-conventional Media Communicationmentioning

confidence: 99%

Qualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond

Pathak,

Pandya,

Bhatia

et al. 2023

IEEE Access

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Utilizing the 0.1 to 10 THz spectrum in the next-generation wireless communication networks holds potential for futuristic applications. However, managing resources to accommodate numerous devices raises privacy and security concerns. Further, technology proliferation entwines devices, infrastructure complexity, and resources. Indeed, the transition from 5G (fifth-generation) to 6G (sixth-generation) signifies a progression towards high-speed data rates, minimal latency, and seamless integration of artificial intelligence, enabling ground-breaking applications and services. However, it complicates network management, privacy, resource allocation, and data processing. Notably, integrating Blockchain Technology (BCT) and Machine Learning (ML) is a promising solution, enhancing security, decentralization, trust in ML decisions, and efficient data sharing. This survey thoroughly reviews the integrated ML and BCT, showcasing their collaborative enhancement of network security, decentralization, trust in ML decisions, immutability, and efficient model sharing. Furthermore, we also delve into various distinctive topics, such as BCT-enabled spectrum refarming, rate splitting multiple access, 6G radar-based communication, reconfigurable intelligent surfaces, visible light communication, and integrated sensing and communication. Moreover, it also explores the integration of ML and BCT in novel 6G communication technologies, including molecular, holographic, and semantic communication. Finally, critical open issues, challenges, solutions, and futuristic scope are identified for forthcoming researchers. INDEX TERMS Terahertz (THz) Communication, Artificial Intelligence (AI), Machine Learning (ML), Blockchain Technology (BCT), and Resource Allocation (RA).

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Section: ML In Non-conventional Media Communicationmentioning

confidence: 99%

Qualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond

Pathak,

Pandya,

Bhatia

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…A variety of detection methods have been proposed to identify OAM patterns, mainly including interferometer diffraction methods [ 8 , 9 , 10 ], which can identify OAM patterns by observing the interference fringe distribution. The diffraction method detects OAM patterns by designing special diffractive optics and measuring the far-field diffraction pattern after the vortex beam passes through the diffractive element.…”

Section: Introductionmentioning

confidence: 99%

Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network

Sun

Huang

et al. 2023

Sensors

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In underwater wireless optical communication (UWOC), a vortex beam carrying orbital angular momentum has a spatial spiral phase distribution, which provides spatial freedom for UWOC and, as a new information modulation dimension resource, it can greatly improve channel capacity and spectral efficiency. In a case of the disturbance of a vortex beam by ocean turbulence, where a Laguerre–Gaussian (LG) beam carrying orbital angular momentum (OAM) is damaged by turbulence and distortion, which affects OAM pattern recognition, and the phase feature of the phase map not only has spiral wavefront but also phase singularity feature, the convolutional neural network (CNN) model can effectively extract the information of the distorted OAM phase map to realize the recognition of dual-mode OAM and single-mode OAM. The phase map of the Laguerre–Gaussian beam passing through ocean turbulence was used as a dataset to simulate and analyze the OAM recognition effect during turbulence caused by different temperature ratios and salinity. The results showed that, during strong turbulence Cn2=1.0×10−13K2m−2/3, when different ω = −1.75, the recognition rate of dual-mode OAM (ℓ = ±1~±5, ±1~±6, ±1~±7, ±1~±8, ±1~±9, ±1~±10) had higher recognition rates of 100%, 100%, 100%, 100%, 98.89%, and 98.67% and single-mode OAM (ℓ = 1~5, 1~6, 1~7, 1~8, 1~9, 1~10) had higher recognition rates of 93.33%, 92.77%, 92.33%, 90%, 87.78%, and 84%, respectively. With the increase in ω, the recognition accuracy of the CNN model will gradually decrease, and in a fixed case, the dual-mode OAM has stronger anti-interference ability than single-mode OAM. These results may provide a reference for optical communication technologies that implement high-capacity OAM.

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Review of vortex beam orbital angular momentum mode detection methods

Lv,

Liu,

Shen

et al. 2024

Materials Today Communications

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Machine Learning Framework Combining Radial Phase Grating and Channel Information-Assisted Underwater Wireless Optical OAM Communications

Cited by 4 publications

References 16 publications

Qualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond

Qualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond

Research on Orbital Angular Momentum Recognition Technology Based on a Convolutional Neural Network

Review of vortex beam orbital angular momentum mode detection methods

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