Atmospheric Turbulence Strength Research Articles

Research on Orbital Angular Momentum Mode Detection in an Atmospheric Environment with Fusion Transfer Learning

The vortex beam carrying Orbital Angular Momentum (OAM) has infinite orthogonal characteristic states, which theoretically can infinitely increase the communication transmission capacity, thus attracting much attention in the field of optical communication. Due to the large amount of data required for training each OAM mode, the increase in channel capacity leads to an exponential growth in the required data volume. At the same time, the phase wavefront distortion caused by atmospheric turbulence (AT) further increases the difficulty of OAM pattern recognition. This article introduces transfer learning into the field of OAM modal detection and establishes an OAM modal classifier for detecting the topological charge of distorted vortex beams. The influence of different data volumes, turbulence intensities, and propagation distances on the accuracy of OAM modal detection during the transmission of Laguerre Gaussian beams in atmospheric turbulent channels is studied, and the generalization ability of the model is analyzed. The results show that compared with traditional convolutional neural networks, the modal classifier proposed in this paper reduces the dataset size to 1/10 of the original and successfully improves the OAM detection accuracy by 15.84%. It also exhibits good generalization under unknown atmospheric turbulence strengths, providing a new approach for identifying OAM modes.

Estimation of ground-based atmospheric turbulence strength (C n2) by neural network architecture

Estimating the atmospheric turbulence strength (C n 2) becomes significant in the research field of electromagnetic radiation transmission through the atmosphere, particularly optical waves. As turbulence strength increases, the quality and strength of these optical waves may decrease and cause much trouble as they pass through the atmosphere. Throughout the years, C n 2 has been formulated by different research groups for various geographical locations and seasons using macro-meteorological variables empirically and theoretically. However, since these models are based on the data collected from numerous places and conditions, such as deserts or coastal areas, they do not provide accurate C n 2 predictions for our experimental site, as demonstrated for three well-known models in the paper. In this study, a novel, to our knowledge, artificial neural network (ANN) model named as quadratic Fourier neural network (QFNN) is trained to estimate C n 2 from experimentally measured ground-based atmospheric turbulence strength and macro-meteorological variables during the winter season in a rural area. The trained model gives reliable estimations, achieving a value of R2=0.92 for experimental C n 2 values.

Evaluating the effectiveness of various diversity and combining techniques on an RF-FSO link

Abstract Diversity and combining is one of the robust techniques to mitigate multi-path fading in RF links and turbulence-induced scintillation in FSO links. This article evaluates the effect of different diversity and combining techniques on outage and BER performance of decode and forward RF-FSO link. We have utilized diversity and combining techniques over source to relay RF link and/or relay to destination FSO link. The selection combining, transmit antenna selection, maximal ratio combining, and maximum ratio transmission schemes are considered. The Monte-Carlo simulation results of outage probability and bit error rate are compared by considering different parameters affecting link quality like the strength of atmospheric turbulence, pointing error, FSO link detection scheme, RF link fading strength, diversity order, etc. It is found that configurations like SIMO-SISO, MISO-SISO, SISO-SIMO, and SISO-MISO, where diversity is applied in either source to relay link or relay to destination link, are improving the overall link performance provided that diversity is applied at the link experiencing stronger fading compared to another link. The link configurations SIMO-MISO or MISO-SIMO also improve the link performance and it can achieve BER of 10−5 at less than 15 dB SNR in favourable link conditions.

Atmospheric turbulence recognition with deep learning models for sinusoidal hyperbolic hollow Gaussian beams-based free-space optical communication links

The integration of artificial intelligence technology to improve the performance of free-space optical communication (FSO) systems has received increasing interest. This study aims to propose a novel approach based on deep learning techniques for detecting turbulence-induced distortion levels in FSO communication links. The deep learning-based models improved and fine-tuned in this work are trained using a dataset containing the intensity profiles of Sinusoidal hyperbolic hollow Gaussian beams (ShHGBs). The intensity profiles included in the dataset are the ones of ShHGBs propagating for 6 km under the influence of six different atmospheric turbulence strengths. This study presents deep learning-based Resnet-50, EfficientNet, MobileNetV2, DenseNet121 and Improved+MobileNetV2 approaches for turbulence-induced disturbance detection and experimental evaluation results. In order to compare the experimental results, an evaluation is made by considering the accuracy, precision, recall, and f1-score criteria. As a result of the experimental evaluation, the average values for accuracy, precision, recall and F-score with the best performance of the improved method are given; average accuracy 0.8919, average precision 0.8933, average recall 0.8955 and average F-score 0.8944. The obtained results have immense potential to address the challenges associated with the turbulence effects on the performance of FSO systems.

A Neural Network Method for Inversion of Turbulence Strength

Accurate inversion of atmospheric turbulence strength is a challenging problem in modern turbulence research due to its practical significance. Inspired by transfer learning, we propose a new neural network method consisting of convolution and pooling modules for the atmospheric turbulence strength inversion problem. Its input is the intensity image of the beam and its output is the refractive index structure constant characterizing the atmospheric turbulence strength. We evaluate the inversion performance of the neural network at different beams. Meanwhile, to enhance the generalisation of the network, we mix data sets from different turbulence environments to construct new data sets. Additionally, the inverted atmospheric turbulence strength is used as a priori information to help identify turbulent targets. Experimental results demonstrate the effectiveness of our proposed method.

Longitudinal spatial coherence of computational ghost imaging through atmospheric turbulence

The resolution and imaging quality of ghost imaging is determined by the longitudinal spatial coherence (LSC) of speckle beams on the signal and reference arms. Based on the cross-correlation function, long-exposure and short-exposure computational ghost imaging through turbulent atmosphere is investigated analytically and numerically in the framework of the traditional imaging theory. According to the point spread function (PSF), the modulation transfer function (MTF) is derived, both of which are utilized to evaluate imaging resolution and imaging quality of computational ghost imaging (CGI), respectively. By simulating long-exposure and short-exposure ghost imaging through atmospheric turbulence, the comprehensive effects of atmospheric turbulence and beam initial parameters on the complex degree of coherence (CDC), PSF, and MTF are studied, respectively. It is found that the degradation of LSC between the two planes on the reference and signal path implies a narrower PSF and increased MTF values, which represent the better resolution and imaging quality. Thus, reducing the atmospheric turbulence strength, the speckle particle size, the wavelength and the propagation distance, and increasing the source size contribute to improving resolution and image quality of CGI because of the degradation of LSC. Furthermore, short-exposure CGI can provide imaging performance superior to long-exposure CGI in terms of resolution and imaging quality due to the decrease of LSC.

Real time characterization of atmospheric turbulence using speckle texture

A new method is proposed for the characterization of the atmospheric turbulence strength parameter () based on the principal component analysis (PCA) of the texture of the speckle intensity pattern obtained on long-range propagation of a focused charge +1 vortex beam. Employing the split-step propagation method, datasets containing instantaneous intensity images of the focused vortex beam are generated for three distinct values, specifically , , and , representing medium to high turbulence levels for a 2 km propagation distance. The gray level co-occurrence matrix (GLCM) methodology is employed to extract key texture attributes, like contrast, correlation, homogeneity, and energy from the intensity images. These extracted texture parameters serve as inputs for training a PCA model, enabling the identification of associated values. The PCA analysis exhibits distinct clustering of the first three principal components for each of the three values, forming individual clusters on the PCA plot. Standard deviational ellipses are drawn to clearly demarcate these clusters on the PCA plot. The texture-based PCA classification of atmospheric turbulence was also performed for a focused Gaussian beam. The comparison of PCA plots between vortex and Gaussian beams showed that a pronounced clear separation of values is obtained for the vortex beam. This indicates that the non-zero orbital angular momentum of the vortex beam also plays an important role in achieving the distinct separation of values on the PCA plot. The proposed method can provide efficient real-time turbulence estimation solely on the basis of texture of the instantaneous intensity speckle with prior training and therefore may simplify the estimation of turbulence strength parameter.

Generation and free-space transmission characterization of bottle vortex beam

Vortex beams carrying orbital angular momentum (OAM) with the doughnut-shaped intensity distribution can be employed in free-space optical (FSO) communication links to circumvent obstructions. However, the size of the receiver aperture is proportional to the size of obstructions, which seriously constrains the application of OAM beams in this scenario. In this paper, we propose a method to generate bottle vortex beams (BVBs) with a parabolic trajectory by manipulating the radial phase distribution of conventional OAM beams. Meanwhile, the trajectory of BVBs generated are highly compatible with the predesigned trajectory by using this method. Moreover, we evaluate the free-space transmission performance of BVBs under atmospheric turbulence and limited receiving aperture. The results show that BVBs have better OAM FSO communication link performance compared with conventional OAM beams and Bessel beams. In addition, the performance of the BVBs circumventing obstructions is further investigated. The simulation results show that when setting the atmospheric turbulence strength D/r0 = 2 and the obstruction size of 40 mm, the average received optical power of the BVBs captured by a limited receiving aperture diameter (d = 40 mm) is improved about 7 dB and 3 dB compared to conventional OAM beams and Bessel beams, respectively.

Vision transformers motivating superior OAM mode recognition in optical communications.

Orbital angular momentum (OAM) has recently obtained tremendous research interest in free-space optical communications (FSO). During signal transmission within the free-space link, atmospheric turbulence (AT) poses a significant challenge as it diminishes the signal strength and introduce intermodal crosstalk, significantly reducing OAM mode detection accuracy. This issue directly impacts the performance of OAM-based communication systems and leads to a reduction in received information. To address this critical bottleneck of low mode recognition accuracy in OAM-based FSO-communications, a deep learning method based on vision transformers (ViT) is proposed for what we believe is for the first time. Designed carefully by numerous experts, the advanced self-attention mechanism of ViT captures more global information from the input image. To train the model, pretraining on a large dataset, named IMAGENET is conducted. Subsequently, we performed fine-tuning on our specific dataset, consisting of OAM beams that have undergone varying AT strengths. The computer simulation shows that based on ViT method, the multiple OAM modes can be recognized with a high accuracy (nearly 100%) under weak-to-moderate turbulence and with almost 98% accuracy even under long transmission distance with strong turbulence (C N2=1×10-14). Our findings highlight that leveraging ViT enables robust detection of complex OAM beams, mitigating the adverse effects caused by atmospheric turbulence.

Statistical law of laser echo disturbance characteristics under atmospheric turbulence

AbstractAtmospheric turbulence significantly affects laser transmission and echo. The size of the echo cross section and the distribution of energy are important parameters to obtain target information. In this paper, the speckle characteristics of laser echo are analyzed using field experiments with different detection distances. The energy attenuation law is observed, and the statistical law governing the cross-sectional area and energy of the echo light spot is determined based on the time variability of atmospheric turbulence. The research shows that in order to obtain stable return laser beam interference information, the current weather situation and the strength of atmospheric turbulence should be considered, and the corresponding interference region should be selected according to the detection distance. Although the size and energy of the echo cross section are directly affected by the detection distance, the variation trend remains consistent and indicates a clear statistical law that provides a strong basis for laser interferometry.

Classification of 270 classes of vector vortex beams using Machine learning-based methods

Vector vortex beams (VVBs) are a promising type of structured light that combine the orbital angular momentum (OAM) and the polarization states of light. Due to their intrinsic high dimensionality, VVBs show great advantages in applications like optical communications, information encryption, and quantum information processing. However, the high dimensionality presents a challenge for pattern detection. In this paper, we compare different machine learning-based methods for classifying 270 classes of VVB using basic CNN, MobileNet, and ResNet18 neural networks. We visualize the VVB modes using a color-coding method with Stokes parameters, and the neural networks’ performance is tested in a 1 km free space communication link with four atmospheric turbulence strengths. The results demonstrate that neural networks can recognize large datasets of laser modes with good accuracies, even under turbulence environments. We also propose an image encryption scheme using the VVB dataset to encode an RGB figure which is transmitted through the turbulence channel and successfully recovered by the pre-trained neural networks. Our study highlights the potential of artificial intelligence for VVB pattern recognition and could have a significant impact on the design of future optical communications systems and information encryption protocols.

Revelation of hidden 2D atmospheric turbulence strength fields from turbulence effects in infrared imaging.

Turbulence exists widely in the natural atmosphere and in industrial fluids. Strong randomness, anisotropy and mixing of multiple-scale eddies complicate the analysis and measurement of atmospheric turbulence. Although the spatially integrated strength of atmospheric turbulence can be roughly measured indirectly by Doppler radar or laser, direct measurement of two-dimensional (2D) strength fields of atmospheric turbulence is challenging. Here we attempt to solve this problem through infrared imaging. Specifically, we propose a physically boosted cooperative learning framework, termed the PBCL, to quantify 2D turbulence strength from infrared images. To demonstrate the capability of the PBCL, we constructed a dataset with 137,336 infrared images and corresponding 2D turbulence strength fields. The experimental results show that cooperative learning brings performance improvements, enabling the PBCL to simultaneously learn turbulence strength fields and inhibit adverse turbulence effects in images. Our work demonstrates the potential of imaging in measuring physical quantity fields.

Atmospheric turbulence strength distribution along a propagation path probed by longitudinally structured optical beams

Atmospheric turbulence can cause critical problems in many applications. To effectively avoid or mitigate turbulence, knowledge of turbulence strength at various distances could be of immense value. Due to light-matter interaction, optical beams can probe longitudinal turbulence changes. Unfortunately, previous approaches tended to be limited to relatively short distances or large transceivers. Here, we explore turbulence probing utilizing multiple sequentially transmitted longitudinally structured beams. Each beam is composed of Bessel-Gaussian ({{{{{{rm{BG}}}}}}}_{{{{{{mathcal{l}}}}}}{{=}}0,{k}_{z}}) modes with different {k}_{z} values such that a distance-varying beam width is produced, which results in a distance- and turbulence-dependent modal coupling to {{{{{mathcal{l}}}}}}{{{{{mathscr{ne }}}}}}0 orders. Our simulation shows that this approach has relatively uniform and low errors (<0.3 dB) over a 10-km path with up to 30-dB turbulence-structure-constant variation. We experimentally demonstrate this approach for two emulated turbulence regions (~15-dB variation) with <0.8-dB errors. Compared to previous techniques, our approach can potentially probe longer distances or require smaller transceivers.

Orbital angular momentum shift-keying communication through atmospheric turbulence and high scattering channel using a deep learning decoder

To increase the data rate and the channel capacity, orbital angular momentum shift keying (OAM-SK) has been studied extensively for free space optical (FSO) communication. However, the beams carrying OAM are disturbed into speckle patterns via the atmospheric turbulence (AT) and high scattering (HS) channel, which causes difficulty in decoding. Herein, we propose an AT and HS channel model to simulate the propagation of OAM beams, showing that the formed speckle size depends on the topological charge l of the OAM mode, which means the OAM mode can be recognized from the receiving speckle patterns. Proof-of-concept​ experiments of an OAM-SK system using a deep learning decoder are implemented. The results show that under dynamic AT with the refractive index structure constant (the AT strength, Cn2=1×10−16K2m−2/3) and static HS (a ground glass), we achieve a hybrid eight-mode OAM-SK, where the decoding accuracy of the encoding set l=1,2,3,4,5,6,7,8 reaches >0.95 within a 500 m transmission distance. Moreover, increasing the encoding mode interval can improve the decoding accuracy; when the AT strength increases to Cn2=1×10−14K2m−2/3 and the scattering media becomes dynamic, the accuracy of the encoding set l=1,4,7,10,13,16,19,22 with a mode interval of 3 can still achieve >0.94 at 100 m. Furthermore, error-free image transmission can be achieved in the OAM-SK system under AT strengths of Cn2=1×10−16K2m−2/3 and 1×10−15K2m−2/3 and HS at 100 m. This work provides a potential approach for FSO communication based on OAM modes under harsh AT and HS channels.

Reliability analysis of optical IRS-enabled FSO system with multiple detectors and PCB under composite turbulence fading channels considering the impact of GML

Free space optical (FSO) communication with optical intelligent reflecting surface (IRS) assistance is one promising and evolving technology that can relax the line-of-sight requirement and enable more efficient and reliable link performance in obstacles areas. In this work, an optical IRS-enabled FSO system is proposed with partially coherent beam (PCB) and multiple detectors. Specifically, for an end-to-end (e2e) IRS-assisted FSO link, the statistical model of optical signal fluctuations due to atmospheric turbulence is assumed to follow Fisher–Snedecor F-distribution, and the random movements of IRS, transmitter (Tx), and receiver (Rx) due to building sway is modeled by geometric and misalignment losses. Considering the Gaussian–Schell model and intensity modulation/direct detection, the analytical average bit error rate (ABER) expressions of the studied system are obtained for a single detector and multiple detectors, respectively, with selected combining (SC), equal gain combining (EGC) and maximum ratio combining (MRC) aperture diversity schemes over independent identically distributed composite fading channels. And the correctness of the analytical results is verified through the Monte-Carlo based statistical simulation method. Results show that the ABER and outage probability performance of IRS-assisted FSO link degrades with the increase in the strength of atmospheric turbulence and building sway. And even the position fluctuations variances of Tx, IRS, and Rx are the same, their impacts on the e2e IRS-assisted FSO link are not the same. Interestingly, aperture diversity techniques can significantly mitigate these adverse effects, especially in the case of MRC scheme. Besides, the studied system with PCB can achieve better system performance compared to that using coherent beam, and the performance gap would become more obvious as the spatial coherence radius decreases. This work could serve as a guide for the design and research of IRS-enabled FSO communication systems.

On the correlated Gamma–Gamma distribution and coherent detection performance of optical spatial modulation

The performance of optical spatial modulation (OSM) is severely damaged by correlated atmospheric turbulence (AT) channels for coherent free-space optical (FSO) communication systems. In this paper, we assume correlated AT channels comply with Gamma–Gamma distribution and present a coherent FSO-OSM system model. Based on this, we derive the closed-form approximated expressions of average symbol error probability (ASEP), and discuss the interaction of signal constellation diagram and spatial constellation diagram of the OSM under different conditions. We further illustrate relations of the ASEP caused by signal constellation and spatial constellation to AT strength and correlation. In addition, we explore the impact trends of the correlation coefficients respectively resulted from large-scale and small-scale eddies on the performance of system under different AT conditions. Through simulations, we prove theoretical results and demonstrate that the rise of AT severity and correlation coefficients can degrade the performance of coherent OSM.

Phase-matching quantum key distribution based on orbital angular momentum under atmospheric turbulence

We propose a phase-matching quantum key distribution (PM-QKD) protocol based on orbital angular momentum (OAM) to exceed the linear key-rate bound without quantum repeater in the paper, named OAM-PM-QKD protocol, where the coherent state consisting of OAM with opposite topological charges is used as the information carrier. With the equivalent entanglement distillation model, we analyze its security and then derive its key generation rate. The numerical simulation results show that the proposed OAM-PM-QKD protocol is able to exceed the linear key-rate bound when the transmission distance exceeds 230 km, and the maximum secure transmission distance is also longer than those of original PM-QKD protocol due to no misalignment in the basis. Furthermore, the influence of the atmospheric turbulence (AT) on the key generation rate is also discussed. The stronger the strength of AT is, the shorter the transmission distance is.

Estimation of atmospheric optical turbulence strength in realistic airborne environments.

In this paper, atmospheric optical turbulence strength is estimated for realistic airborne environments using a modified phase-variance approach, as well as a modified slope-discrepancy approach. Realistic airborne environments are generated using wave-optics simulations of a plane wave propagating through increasing strengths of homogeneous atmospheric optical turbulence, both with and without aero-optical contamination (from in-flight wavefront sensor data) and additive-measurement noise. In comparison to the modified phase-variance approach, the results show that the modified slope-discrepancy approach more accurately estimates atmospheric optical turbulence strength over a wide range of conditions. Such results are encouraging for realistic airborne environments because they can be scaled to different freestream conditions as long as the boundary layer is considered canonical.

Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City

The foundation of any smart city requires an innovative and robust communication infrastructure. Many research communities envision free-space optical communication (FSO) as a promising backbone technology for the services and applications provided by such cities. However, the channel through which the FSO signal travels is the atmosphere. Therefore, the FSO performance is limited by the local weather conditions. The variation in meteorological variables leads to variations of the refractive index along the transmission path. These index inhomogeneities (i.e., atmospheric turbulence) can significantly degrade the performance of FSO systems. Thus, a practical implementation of the FSO link must carefully consider the atmospheric turbulence effect. This paper aims to investigate the feasibility of FSO communication for NEOM, a promising smart city in Saudi Arabia. We study the effect of weather conditions on FSO links using the micrometeorology model, taking into account actual weather data. The FSO performance in winter and summer was compared in terms of the bit error rate, signal-to-noise ratio (SNR), link availability, and transmission distance. The study shows that the atmospheric turbulence strength is moderate and strong in winter and summer, respectively. The temperature has the biggest impact on the FSO system when compared to the other meteorological elements included in this study. Furthermore, at transmission distances less than 300 m, atmospheric turbulence does not significantly affect the FSO for the operating wavelength of 1550 nm. Furthermore, it has been shown that at transmission distances greater than 300 m, the SNR in summer is more than 18% higher than in winter. The findings of this research enable understanding of the effect of turbulence caused by NEOM weather on the FSO link, thus assisting engineers in establishing a reliable FSO backbone link by adjusting the relevant parameters.

Comparative analysis of some Schell-model beams propagating through turbulent atmosphere

In this paper, we investigate a comparative analysis of some Generalized Laguerre–Gaussian Schell-model beams through a paraxial ABCD optical system in a turbulent atmosphere. Based on the extended Huygens–Fresnel diffraction integral, analytical expressions for the spectral density in the receiver plane of the studied beams are derived in detail. By studying the effects of the source coherence parameters and the atmospheric turbulence strength, the numerical results indicate that the profile of these Schell-model beams takes different shapes during their propagation.