Turbulence correction with artificial neural networks

Lohani, Sanjaya; Glasser, Ryan T.

doi:10.1364/ol.43.002611

Cited by 127 publications

(65 citation statements)

References 23 publications

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“…In [302], Li et al presented a technique for the detection of atmospheric turbulence and adaptive modulation using CNN achieving high accuracy for different numbers of LG modes. Lohani and Glasser designed a feedback scheme based on CNNs to precorrect OAM profiles at the transmitter end before propagating through turbulent atmosphere, which significantly enhanced the identification process at the receiver end [303]. Authors in [304] proposed the use of CNN to detect OAM beams subject to turbulence and misalignment errors between the transmitter and the receiver.…”

Section: Machine Learning-based Oam Recovery Approachmentioning

confidence: 99%

Communicating Using Spatial Mode Multiplexing: Potentials, Challenges, and Perspectives

Trichili

Park

Zghal

et al. 2019

IEEE Commun. Surv. Tutorials

183

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Time, polarization, and wavelength multiplexing schemes have been used to satisfy the growing need of transmission capacity. Using space as a new dimension for communication systems has been recently suggested as a versatile technique to address future bandwidth issues. We review the potentials of harnessing the space as an additional degree of freedom for communication applications including free space optics, optical fiber installation, underwater wireless optical links, on-chip interconnects, data center indoor connections, radio frequency and acoustic communications. We focus on the orbital angular momentum (OAM) modes and equally identify the challenges related to each of the applications of spatial modes and the particular OAM modes in communication. We further discuss the perspectives of this emerging technology. Finally, we provide the open research directions and we discuss the practical deployment of OAM communication links for different applications. ). 4 in the future. We also summarize the open problems and the future research directions. A discussion of the practicability and cost of multi-OAM communication is also included. II. OAM POTENTIALS A. Free Space Optical CommunicationFSO is a license free wireless communication configuration that has recently received much interest for a variety of applications. FSO is an attractive solution for last mile connectivity problems, particularly for communication networks, when the installation of fiber optics is costly or not possible [66]. FSO can be also used to establish inter-building secure communication, and can be deployed as a backup to optical fibers. Wireless optical communication can guarantee a line-of-sight (LoS) high bit rate wireless transmission over long distances, up to several kilometers. Furthermore, FSO communication is considered as a promising technique to scale down bandwidth challenges in future 5G networks [67].Multiple wavelength FSO provides better transmission, as demonstrated in [68], [69]. Data can be mapped on advanced modulation formats to achieve high bit rates and high spectral efficiency levels [70]- [72]. Another option is multiple-input and multiple-output (MIMO) FSO communication in which multiple lasers are positioned to transmit Gaussian beams to multiple receiving apertures [73]. Over the last few years, it has been proven that it is possible to transmit information over spatially structured light beams [21], [74], [75] including OAM beams and plane waves. Our focus is on the OAM modes of light.The first PoC communication experiment incorporating OAMs in free space was carried out in 2004 by Gibson and co-workers [21]. Over a 15-meter long link, 8 -spaced values were chosen along the Gaussian beam as an alphabet for the communication. OAM beams were generated and detected using two light modulators. Since then, much progress has been made and free space transmission capacity of over 1 Tbit/s is, today, possible [76]- [78]. By performing three dimensional multiplexing, Huang and co-workers were able to attain a 100 Tbi...

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Section: Machine Learning-based Oam Recovery Approachmentioning

confidence: 99%

Communicating Using Spatial Mode Multiplexing: Potentials, Challenges, and Perspectives

Trichili

Park

Zghal

et al. 2019

IEEE Commun. Surv. Tutorials

183

View full text Add to dashboard Cite

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“…Zhao et al further demonstrated the potential of a CNN method for the detection of OAM modes subject to turbulence and misalignment, using simulated data [22]. Turbulence regression CNN is reported in [23], where the estimated turbulence impairment is fed back to the transmitter in order to achieve impairment-free transmission of OAM modes. A CNN classifier was used in [24] to detect 21 laboratory-generated HG modes with different input beam parameters.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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The unique orthogonal shapes of structured light beams have attracted researchers to use as information carriers. Structured light-based free space optical communication is subject to atmospheric propagation effects such as rain, fog, and rain, which complicate the mode demultiplexing process using conventional technology. In this context, we experimentally investigate the detection of Laguerre Gaussian and Hermite Gaussian beams under dust storm conditions using machine learning algorithms. Different algorithms are employed to detect various structured light encoding schemes including the use of a convolutional neural network (CNN), support vector machine, and k-nearest neighbor. We report an identification accuracy of 99% under a visibility level of 9 m. The CNN approach is further used to estimate the visibility range of a dusty communication channel.

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“…Our work makes significant steps forward with respect to previous endeavours: while Refs. [53][54][55][56][57][58] leverage NNs to process OAM states, our work is the first to tackle VVBs. Moreover, owing to the variety of techniques we deploy, we can address both classification and regression tasks, thus enabling the reconstruction of the input states in relevant cases of structured light beams.…”

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confidence: 99%

Machine Learning-Based Classification of Vector Vortex Beams

et al. 2020

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Structured light is attracting significant attention for its diverse applications in both classical and quantum optics. The so-called vector vortex beams display peculiar properties in both contexts due to the non-trivial correlations between optical polarization and orbital angular momentum. Here we demonstrate a new, flexible experimental approach to the classification of vortex vector beams. We first describe a platform for generating arbitrary complex vector vortex beams inspired to photonic quantum walks. We then exploit recent machine learning methods -namely convolutional neural networks and principal component analysis -to recognize and classify specific polarization patterns. Our study demonstrates the significant advantages resulting from the use of machine learning-based protocols for the construction and characterization of high-dimensional resources for quantum protocols.Introduction-Light is endowed with Orbital Angular Momentum (OAM) [1, 2], a degree of freedom associated with structured, non-plane wavefronts, and characterized by an azimuthal phase dependence. When a nontrivial phase dependence is coupled with a helicoidal transverse polarization pattern, one talks of a Vector Vortex Beam (VVB) [2,3]. The interest in such states is motivated by the applications in multiple fields of classical and quantum optics [4,5]: from particle trapping to metrological applications in microscopy [6,7], and for OAM-based communications schemes in free-space and in-fibre [8,9]. VVBs are also often employed in quantum information protocols due to the hyperentanglement between their polarization and spatial degrees of freedom. Photonic platforms for quantum sensing and metrology leveraging such encoding have also been reported [10,11]. OAM-based schemes for investigating quantum causal structures [12], quantum communication and cryptography [13][14][15][16][17][18], quantum walks [19][20][21], quantum simulation [22,23], and quantum state engineering [24,25], have been previously demonstrated.Despite the potential of VVBs, many questions regarding the decoding of information stored in OAM and polarization remain unanswered. Various techniques of OAMdemultiplexing envisage the need of additional instruments -such as interferometry [26][27][28] or spatial filtering [29][30][31] -to be efficiently implemented. These introduce detrimental effects of loss and noise [32]. Moreover, the challenge of performing state tomography in such a high-dimensional framework, a fundamental task in quantum information processing [33,34], can hardly be overestimated. The design and demonstration of reliable techniques for the generation and classification of VVBs is thus highly desirable. Indeed, substantive efforts on finding novel platforms are subject of intense research activities [6,7,35,36], including in integrated photonics [37][38][39] and generation by plasmonic metasurfaces [40,41].Recently, Machine Learning (ML) has emerged as a versatile toolbox to tackle a variety of tasks arising in experi-

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Turbulence correction with artificial neural networks

Cited by 127 publications

References 23 publications

Communicating Using Spatial Mode Multiplexing: Potentials, Challenges, and Perspectives

Communicating Using Spatial Mode Multiplexing: Potentials, Challenges, and Perspectives

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

Machine Learning-Based Classification of Vector Vortex Beams

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