Orbital Angular Momentum Mode Detection Research Articles

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.

Highly accurate OAM mode detection network for ring Airy Gaussian vortex beams disturbed by atmospheric turbulence based on interferometry.

Atmospheric turbulence reduces the detection accuracy of orbital angular momentum (OAM) modes, which affects the performance of OAM optical communication. In this paper, we propose a method based on interferometry and a residual network (ResNet) to detect the OAM modes of ring Airy Gaussian vortex beams (RAGVBs) disturbed by atmospheric turbulence. The RAGVBs first interfere with spherical waves to obtain the sign features of the OAM modes, and then ResNet is employed to recognize OAM modes from the interferograms. The results demonstrate that the detection accuracy is higher than that of the OAM spectrum method under different turbulence strengths. The detection accuracy can even reach over 99% under strong fluctuations. Our research provides a reference for improving the performance of OAM optical communication through atmospheric turbulence.

Mode Recognition of Orbital Angular Momentum Based on Attention Pyramid Convolutional Neural Network

In an effort to address the problem of the insufficient accuracy of existing orbital angular momentum (OAM) detection systems for vortex optical communication, an OAM mode detection technology based on an attention pyramid convolution neural network (AP-CNN) is proposed. By introducing fine-grained image classification, the low-level detailed features of the similar light intensity distribution of vortex beam superposition and plane wave interferograms are fully utilized. Using ResNet18 as the backbone of AP-CNN, a dual path structure with an attention pyramid is adopted to detect subtle differences in the light intensity in images. Under different turbulence intensities and transmission distances, the detection accuracy and system bit error rate of basic CNN with three convolution layers and two full connection layers, i.e., ResNet18 and ResNet18, with a specified mapping relationship and AP-CNN, are numerically analyzed. Compared to ResNet18, AP-CNN achieves up to a 7% improvement of accuracy and a 3% reduction of incorrect mode identification in the confusion matrix of superimposed vortex modes. The accuracy of single OAM mode detection based on AP-CNN can be effectively improved by 5.5% compared with ResNet18 at a transmission distance of 2 km in strong atmospheric turbulence. The proposed OAM detection scheme may find important applications in optical communications and remote sensing.

Nanophotonic Materials for Twisted-Light Manipulation.

Twisted light, an unbounded set of helical spatial modes carrying orbital angular momentum (OAM), offers not only fundamental new insights into structured light-matter interactions, but also a new degree of freedom to boost optical and quantum information capacity. However, current OAM experiments still rely on bulky, expensive, and slow-response diffractive or refractive optical elements, hindering today's OAM systems to be largely deployed. In the last decade, nanophotonics has transformed the photonic design and unveiled a diverse range of compact and multifunctional nanophotonic devices harnessing the generation and detection of OAM modes. Recent metasurface devices developed for OAM generation in both real and momentum space, presenting design principle and exemplary devices, are summarized. Moreover, recent development of whispering-gallery-mode-based passive and tunable microcavities, capable of extracting degenerate OAM modes for on-chip vortex emission and lasing, is summarized. In addition, the design principle of different plasmonic devices and photodetectors recently developed for on-chip OAM detection is discussed. Current challenges faced by the nanophotonic field for twisted-light manipulation and future advances to meet these challenges are further discussed. It is believed that twisted-light manipulation in nanophotonics will continue to make significant impact on future development of ultracompact, ultrahigh-capacity, and ultrahigh-speed OAM systems-on-a-chip.

Interferometric orbital angular momentum mode detection in turbulence with deep learning.

Orbital angular momentum (OAM) modes are topical due to their versatility, and they have been used in several applications including free-space optical communication systems. The classification of OAM modes is a common requirement, and there are several methods available for this. One such method makes use of deep learning, specifically convolutional neural networks, which distinguishes between modes using their intensities. However, OAM mode intensities are very similar if they have the same radius or if they have opposite topological charges, and as such, intensity-only approaches cannot be used exclusively for individual modes. Since the phase of each OAM mode is unique, deep learning can be used in conjugation with interferometry to distinguish between different modes. In this paper, we demonstrate a very high classification accuracy of a range of OAM modes in turbulence using a shear interferometer, which crucially removes the requirement of a reference beam. For comparison, we show only marginally higher accuracy with a more conventional Mach-Zehnder interferometer, making the technique a promising candidate towards real-time, low-cost modal decomposition in turbulence.

Orbital angular momentum mode detection of the combined vortex beam generated by coherent combining technology.

Coherent beam combining (CBC) technology has distinct advantages in generating high power vortex beam. In this paper, a circularly arranged coherent beam array (CBA) with discrete vortex phases is constructed to generate vortex beams. We demonstrated that the combined vortex beam (CVB) generated by the CBA is a multiplexing vortices optical field, which sidelobe is the coaxial interference pattern of these spiral harmonic components. Using the designed Dammam vortex grating (DVG), the orbital angular momentum (OAM) spectrum of the CVB is detected. Moreover, taking the target OAM mode purity of the CVB as the evaluation function of active phase control system, we realized the closed-loop phase control of the CBA and obtained the phase-locked output of the CVB.

Analysis of the transmission characteristics of Hank-Bessel beam in anisotropic ocean turbulence

Based on the Rytov approximation theory, we analyze the cross-spectral density of Hankel-Bessel (HB) beams in anisotropic ocean turbulence. In this paper, we study the orbital angular momentum (OAM) mode detection probability, the crosstalk probability and the spiral phase spectrum of the HB beam, and establish the OAM mode detection probability model in anisotropic ocean turbulence. The results show that the detection probability of the emission mode is decreased and the spiral phase spectrum is expanded due to the ocean turbulence. Furthermore, with the increase of anisotropy factor, the influence of ocean turbulence on the detection probability of HB beam becomes smaller. Meanwhile, with the increase of the temperature variance dissipation rate and the equilibrium parameter, and the decrease of the dynamic energy dissipation rate, the influence of ocean turbulence on the orbital angular momentum transmission is increased.

Broad Bandwidth and Highly Efficient Recognition of Optical Vortex Modes Achieved by the Neural-Network Approach

High accuracy recognition of the orbital angular momentum (OAM) of light based on petal interference patterns is demonstrated using a convolutional neural network (CNN) approach with an improved Alexnet structure. A type of hybrid beam carrying OAM is utilized to provide more controllable degrees of freedom to recognize the OAM of light. The relationship between the training sample resolution (or the number associated with the accuracy) and the training time of the model, is presented. The recognition accuracy is closely related with the quantum number l of OAM, the angular ratio n of the spire phase over the hybrid phase in one modulation period, and the propagation distance z. Our studies show that when l ranges from 1 to 10, and n varies from 0.02 to 0.99, the recognition accuracy rate of OAM is nearly 100%. The minimum interval of n recognized at the OAM modes decreases to 0.01, which shows the super-high bandwidth of the generation and detection of OAM modes. Such results suggest great potential for the next generation of CNN-based OAM optical communication applications.

Convolutional Neural Network Assisted Optical Orbital Angular Momentum Identification of Vortex Beams

The rapid and accurate identification of large-scale orbital angular momentum (OAM) modes is crucial for expanding the application of vortex beams (VBs). In this paper, an OAM mode recognition method based on convolutional neural networks (CNNs) is proposed and investigated. We construct an 8-layer CNN possesses complex feature extraction capability and train it to own powerful anti-turbulence competence by feeding the intensity patterns of VBs interfered by Gaussian beam. After supervised training of a large sample set, the CNN model takes on excellent network generalization ability and can well detect VBs with the mode range of [−50,50]. The simulation results indicate that under the influence of weak and medium turbulences, the average recognition accuracy exceeds 99%. Even under strong turbulence, the accuracy also reaches 98.54%. Meanwhile, the identification time is only 1.55ms per OAM mode with Intel(R) Xeon(R) Gold 6148 CPU. Moreover, the influence of different Gaussian beam waists, VB orders, input training sets, and CNN structures on OAM mode recognition performance, is fully studied. These results demonstrate that our proposed method can achieve higher accuracy and higher order OAM mode detection at a fast speed, which contributes a more effective method for the recognition of VBs.

Mode detection of misaligned orbital angular momentum beams based on convolutional neural network.

The utilization of beam-carrying orbital angular momentum (OAM) for free-space optical (FSO) communication can increase channel capacity. However, the misalignment of the beam is an effect that must be mitigated in FSO communication systems. Due to the robustness of deep learning technology in pattern recognition, a neural network structure is proposed and improved to mitigate the effect of misalignment error. First, compared with the simple convolutional neural network proposed, data augmentation is adopted in the training. Then, a view-pooling layer is added after the convolutional layer. This layer can longitudinally compress feature maps from multiple receiving angles. In order to verify the performance of the proposed method, related experiments are reported in this paper. It can be seen from the results that when the tilt angle is less than 35°, the accuracy of OAM mode detection is above 99%, 93%, and 88%, respectively, corresponding to the condition of weak (Cn2=1×10-15 m-2/3), medium (Cn2=1×10-14 m-2/3) and strong (Cn2=1×10-13 m-2/3) turbulence.

Detecting the Orbital Angular Momentum of Electro-Magnetic Waves Using Virtual Rotational Antenna

Orbital Angular Momentum (OAM) is a typical spatial mode of an Electro-Magnetic (EM) wave. Correctly detecting the OAM mode is fundamental and of foremost importance when applying the phenomenon to wireless transmission in free space. It is found that when rotating an OAM wave, a rotational Doppler shift that is proportional to the rotation speed and the OAM mode number can be observed. This property can be used for OAM detection, i.e., different OAM modes are identified by measuring the corresponding rotational Doppler frequency shifts. In previous work, this method was implemented by mechanically rotating the OAM wave, resulting in a small frequency shift. Since the high-speed mechanical rotation is hard to manufacture in a real system, it brings limitations to the bandwidth for each OAM wave. In this paper, we report on an OAM mode detection method based on digitally rotating a virtual antenna. The transmitter and receiver are physically fixed, but the Virtual Rotational Antenna (VRA) is obtained by interpolating the signals received from transverse-mounted receiving antennas. A large rotational Doppler shift occurs as a consequence of using digital processing, resulting in more capability for wideband wireless data transmission with the larger shifted frequency.

Propagation of an optical vortex carried by a partially coherent Laguerre-Gaussian beam in turbulent ocean.

The analytical formulas for the orbital angular momentum (OAM) mode probability density, signal OAM mode detection probability, and spiral spectrum of partially coherent Laguerre-Gaussian (LG) beams with optical vortices propagation in weak horizontal oceanic turbulent channels were developed, based on the Rytov approximation theory. The effect of oceanic turbulence and beam source parameters on the propagation behavior of the optical vortices carried by partially coherent LG beams was investigated in detail. Our results indicated that optical turbulence in an ocean environment produced a much stronger effect on the optical vortex than that in an atmosphere environment; the effective range of the signal OAM mode of LG beams with a smaller ratio of the mode crosstalk was limited to only several tens of meters in turbulent ocean. The existence of oceanic turbulence evidently induced OAM mode crosstalk and spiral spectrum spread. The effects of oceanic turbulence on the OAM mode detection probability increased with the increase of radial and azimuthal mode orders, oceanic turbulent equivalent temperature structure parameter, and temperature-salinity balance parameter. The spatial partial coherence of the beam source would enhance the effect of turbulent aberrations on the signal OAM mode detection probability, and fully coherent vortex beams provided better performance than partially coherent ones. Increasing wavelength of the vortex beams would help improve the performance of this quantum optical communication system. These results might be of interest for the potential application of optical vortices in practical underwater quantum optical communication among divers, submarines, and sensors in the ocean environment.