Adaptive Demodulator Using Machine Learning for Orbital Angular Momentum Shift Keying

Li, Jin; Zhang, Min; Wang, Danshi

doi:10.1109/lpt.2017.2726139

Cited by 82 publications

(53 citation statements)

References 15 publications

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“…Três sub-cenários foram criados tentando elucidar quais seriam as melhores escolhas para compor um alfabeto de 32 símbolos. No Cenário 2.1, foram selecionados valores baixos de ±l 1 e ±l 2 : {(1, 2), (1,3), (1,4), (1,5), (1,6), (1,7), (1,8), (1,9), (2,3), (2,4), (2,5), (2,6), (2,7), (2,8), (2,9), (3,4), (3,5), (3,6), (3,7), (3,8), (3,9), (4,5), (4,6), (4,7), (4,8), (4,9), (5,6)...…”

Section: Metodologia E Resultadosunclassified

“…O emprego de aprendizado de máquina para demodulação adaptativa de um sistema OAM (do inglês Orbital Angular Momentum) foi recentemente proposto em [8]. Os autores apresentaram resultados de desempenho para a simulação de diferentes condições de turbulência atmosférica, diferentes distâncias e diferentes algoritmos de aprendizado de máquina.…”

Section: Introductionunclassified

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Utilização de Aprendizado de Máquina na Demodulação de Sistemas OAM utilizando Multiplexação de Modos Orbitais

Runge¹,

Portugheis²,

Dias³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Este trabalho apresenta resultados de simulação para um sistema de transmissãoóptica OAM no espaço livre sob efeito de turbulência atmosférica. Os momentos angulares orbitais, correspondendo aos símbolos de transmissão, são utilizados para modular um feixe de luz no modo Laguerre-Gauss. Na recepção, imagens simulando o que seria capturado por uma câmera CCD são analisadas por uma rede neural convolucional (CNN), o que corresponde ao processo de demodulação e classificação dos símbolos recebidos. Foram simuladas as transmissões de alfabetos utilizando superposição simples e utilizando tuplas formadas por multiplexação de superposições simples de dois momentos orbitais. Os resultados de simulação apontam desempenho similar dos dois casos nas condições de turbulência fraca e moderada. Este fato pode conduzir a possíveis ganhos de capacidade para sistemas OAM que utilizem alfabetos obtidos através da multiplexação de superposições simples de momentos angulares orbitais.Palavras-Chave-Momento angular orbital, rede neural convolucional, aprendizado de máquina, turbulência atmosférica.Abstract-This work presents the simulation results for an OAM optical transmission system in the free space under the effect of atmospheric turbulence. The orbital angular moments, corresponding to the transmission symbols, are used to modulate a light beam in Laguerre-Gauss mode. At the reception side, images simulating what is captured by a CCD camera are analyzed by a convolutional neural network in a process of demodulation and classification of received symbols. Simulations are performed using transmission of alphabets formed by tuples obtained by simple superposition of orbital modes, and by mutiplexing two superposed orbital modes. The simulation results point to similar performance for the cases of systems using simple superposition and using tuples formed by multiplexing moments in the conditions of weak and moderate turbulence. These results may lead to possible capacity gains for OAM systems using alphabets obtained through multiplexing simple superposed orbital angular moments.

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Section: Metodologia E Resultadosunclassified

Section: Introductionunclassified

Utilização de Aprendizado de Máquina na Demodulação de Sistemas OAM utilizando Multiplexação de Modos Orbitais

Runge¹,

Portugheis²,

Dias³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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“…However, on one hand, for some previous relative researches [20] [24], high recognition accuracy (RA) of OAM comes at a cost of high system complexity or huge computational cost. On the other hand, in some previous works [22] [25], though the general system complexity is acceptable, the RA is relatively poor. Hence, appropriate balance between computing complexity of the CNN model and RA are required.…”

Section: Volume XX 2017mentioning

confidence: 85%

“…Using CNN technique, a combination of AT detection and adaptive demodulation was realized in Ref. [21]. In Ref.…”

Section: Volume XX 2017mentioning

confidence: 99%

“…On the other hand, if the CNN model is too shallow, it will fail to efficiently extract the features of images and thus produce poor RA. Many previous researches have demonstrated that selecting the number of layers as 6 can not only guarantee higher RA than CNN models with fewer layers [21], [30,31] but also avoid high computational complexity or overfitting issue caused by excessive number of layers [16], [20], [22]. Therefore, in order to ensure high recognition accuracy and avoid huge computational complexity and severe overfitting issue, we select the number of layers of our CNN model as 6.…”

Section: A Overall Description Of the Simulated Oam Transmission Systemmentioning

confidence: 99%

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High-Accuracy Recognition of Orbital Angular Momentum Modes Propagated in Atmospheric Turbulences Based on Deep Learning

Hao

Zhao

et al. 2020

IEEE Access

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The atmospheric turbulence (AT) causes distortion of phase fronts of orbital angular momentum (OAM) beams, which hinders the recognition of OAM modes. Convolutional neural network (CNN), a deep learning (DL) technique, can be utilized to realize the effective recognition of OAM modes. In this article, we propose a properly designed six-layer CNN model that can achieve high recognition accuracy (RA) of OAM modes at a reasonable computing complexity. We used intensity images of Laguerre-Gaussian (LG) beams to train our CNN model and explored the relationship between the RA for different single OAM modes and many factors including the number of training epochs, AT intensity, transmission distance, and the number of single OAM modes. Our CNN model can obtain an RA of 97.1% under moderate turbulence and 80% under strong turbulence, which are better than some CNN models proposed previously. Compared with these previous CNN models, our CNN model can also reduce the time consumption for at most 70%. Our research could contribute in achieving higher data capacity in OAM-based free space optical (FSO) communication systems. INDEX TERMS atmospheric turbulence (AT), convolutional neural network (CNN), free-space optical (FSO) communication, orbital angular momentum (OAM)

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Decoding Optical Data with Machine Learning

Fang

Swain

Unni

et al. 2020

Laser & Photonics Reviews

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Optical spectroscopy and imaging techniques play important roles in many fields such as disease diagnosis, biological study, information technology, optical science, and materials science. Over the past decade, machine learning (ML) has proved promising in decoding complex data, enabling rapid and accurate analysis of optical spectra and images. This review aims to shed light on various ML algorithms for optical data analysis with a focus on their applications in a wide range of fields. The goal of this work is to sketch the validity of ML‐based optical data decoding. The review concludes with an outlook on unaddressed problems and opportunities in this emerging subject that interfaces optics, data science, and ML.

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Adaptive Demodulator Using Machine Learning for Orbital Angular Momentum Shift Keying

Cited by 82 publications

References 15 publications

Utilização de Aprendizado de Máquina na Demodulação de Sistemas OAM utilizando Multiplexação de Modos Orbitais

Utilização de Aprendizado de Máquina na Demodulação de Sistemas OAM utilizando Multiplexação de Modos Orbitais

High-Accuracy Recognition of Orbital Angular Momentum Modes Propagated in Atmospheric Turbulences Based on Deep Learning

Decoding Optical Data with Machine Learning

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