Transmission of Orbital Angular Momentum and Cylindrical Vector Beams in a Large-Bandwidth Annular Core Photonic Crystal Fiber

Sharma, Manish Dev; Amirkhan, F.; Mishra, Satyendra K.; Sengupta, Dipankar; Messaddeq, Younès; Blanchard, F.; Ung, Bora

doi:10.3390/fib8040022

Cited by 7 publications

(1 citation statement)

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“…In a recently constructed annular core photonic crystal fiber (AC-PCF), the CCD cameras were used in stable OAM propagation and cylindrical vector beams. Many applications in spacedivision multiplexing, optical sensing, and tracking are possible with this method [31]. These cameras were used in OAM-SK underwater wireless optical communication (UWOC) systems in oceanic turbulence channels [32].…”

Section: Multiple Vf Transmission Through the Proposed Systemmentioning

confidence: 99%

Performance Analysis of 3D Video Transmission Over Deep-Learning-Based Multi-Coded N-ary Orbital Angular Momentum FSO System

et al. 2021

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Orbital angular momentum-shift keying (OAM-SK), which is the rapid switching of OAM modes, is vital but seriously impeded by the deficiency of OAM demodulation techniques, particularly when videos are transferred over the system. Thus, in this paper, 3D chaotic interleaved multi-coded video frames (VFs) are conveyed via an N-OAM-SK free space optic (FSO) communication system to enhance the reliability and efficiency of the model. To tackle the defects of the OAM-SK-FSO mechanism, two efficient deep learning (DL) techniques, namely convolution recurrent neural network (CRNN) and 3D convolution neural network (3DCNN) are used to decode OAM modes with a lower BER. Moreover, a graphics processing unit (GPU) is used to accelerate the training process with slight power consumption. The utilized datasets for OAM states are generated by applying different scenarios using trial and error method. The simulation results imply that LDPC coded VFs achieve the greatest peak signal-to-noise ratios (PSNRs) and the lowest BERs using the 16-OAM-SK model. Both 3DCNN and CRNN techniques have nearly the same performance, but this performance deteriorates in the case of larger dataset classes. Moreover, the GPU accelerates the performance by almost 67.6% and 36.9% using CRNN and 3DCNN techniques, respectively. These two DL techniques are more effective in evaluating the classification accuracy than the other traditional techniques by almost 10 − 20%.

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Section: Multiple Vf Transmission Through the Proposed Systemmentioning

confidence: 99%