Orbital Angular Momentum Shift Keying Research Articles

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.

Ultralow-power spiking neural networks for 1024-ary orbital angular momentum shift keying free-space optical communication

The theoretical unbounded orbital angular momentum (OAM) states can be exploited as data bits in the OAM shift keying (OAM-SK) free-space optical (FSO) communications. In order to cope with the atmospheric turbulence (AT) and misalignment in practical applications, various machine learning algorithms, or neural networks (NNs), have been put forward to decode the OAM states. However, to recognize the hybrid spatial modes representing a large bit states, the massive learnable nodes, longer computation time and more training parameters are required to improve the capability of the NNs, resulting in energy efficiency burden to the hardware device. In this paper, the event-based spiking neural network (SNN) is utilized to recognize the hybrid spatial modes consisting of superposed coaxial Laguerre–Gaussian modes with l ranging from 0 to 9 and p = 0, which is termed as spiking OAM-recognition neural network (Spiking-ORNN). In comparison to the previous solution of running deep NNs on graphics processing units, the neuromorphic solution of running Spiking-ORNN on neuromorphic chips exhibits 4300× higher energy efficiency without obvious sacrifice of recognition accuracy (less than 0.5%). Moreover, we experimentally demonstrate a 10 m 1024-ary OAM-SK FSO communication for the transmission of an image with a 10 bit grey level, wherein the peak signal-to-noise ratio of the received image can exceed 41.4 dB under the AT of =10−15 m−2/3. We anticipate that our results can stimulate further researches on the utilization of the brain-like SNN chips to reduce the energy consumptions based on the artificial-intelligence-enhanced optoelectronic systems.

Orbital angular momentum mode demodulation with neural network-assisted coherent nanophotonic circuits

Vortex beam carrying orbital angular momentum (OAM) shows promising potential in enlarging communication capacity and improving modulation ability for its mode orthogonality. Although various OAM multiplexing schemes have been proposed to boost channel capacity density, few studies have focused on OAM shift keying (OAM-SK) communication because of the lack of rapid and low-latency mode demodulation technologies. In this study, we propose a neural-network-assisted coherent nanophotonic circuit (CNC) strategy for demodulating OAM modes and investigate its application in OAM-SK communication. The CNC comprises 380 Mach–Zehnder interferometers (MZIs) and achieves a unitary matrix transformation by simultaneously modulating the amplitude and phase of optical pulses via multi-light interference, where a neural network-assisted design algorithm is used to obtain the phase parameters in the CNC that satisfy the mapping relationship between feature information and OAM modes. Since the satisfied mapping relationship is performed in the optical domain, OAM modes can be demodulated at the speed of light. We show that this CNC can effectively identify the OAM modes ranging from -10 to +10, and the identification accuracy exceeds 90% under moderate turbulence intensity. As a proof of concept, we simulated a hexadecimal OAM-SK communication link, and the OAM signals under turbulent perturbations were successfully demodulated with an accuracy of 99.33%.

Coherent demodulated underwater wireless optical communication system based on convolutional neural network

The optical vortex beams carrying orbital angular momentum (OAM) is a beam with a special transverse spatial distribution, which has infinite orthogonal properties and can significantly improve the channel capacity of underwater wireless optical communication (UWOC) systems. However, for the underwater wireless optical communication system based on orbital angular momentum shift keying (UWOC-OAM-SK), turbulence in the seawater channel can distort the optical vortex beam and reduce the ability of the system to demodulate the OAM-SK signal, which can reduce the reliability of the system. To improve the performance of UWOC-OAM-SK systems in the channel with different turbulence, a coherent demodulation UWOC-OAM-SK system using an image recognizer based on convolutional neural network (CNN) as demodulator is proposed in this paper. Compared with the incoherent system, the proposed system can obtain a detection image with higher image contrast and more pattern features before the OAM mode is demodulated from the OAM-SK signal at the receiver, therefore has higher reliability. Moreover, the proposed system can recognize mutually conjugate OAM modes, which can greatly save the multiplexing resources in OAM communication. With the excellent image-data processing capability of CNN, the coherent demodulator based on CNN image recognizer has higher demodulation accuracy than the incoherent demodulator in both long distance channels or strongly turbulence channels underwater. The simulation results show that the demodulation accuracy of the proposed system is still higher than 90% in channels with strong turbulence intensity Cn2=1×10−14(K2m−2/3) or transmission distance z=80m. The work done provides some theoretical basis for the design of the UWOC-OAM-SK system.

Robust neural network-assisted conjugate orbital angular momentum mode demodulation for modulation communication

Orbital angular momentum shift-keying (OAM-SK), which enables modulation of digital signals by rapidly switching OAM modes, is promising in improving the modulation ability and confidentiality. Robust identification of conjugate OAM modes of VBs perturbed by atmospheric turbulence is critical for efficient demodulation of OAM-SK signals. We experimentally investigate a convolutional neural network (CNN) approach to detect OAM modes from diffraction patterns of turbulence-induced vortex beams. By introducing the hybrid-turbulence with variable turbulence strengths, this approach possessed high robustness against turbulence and low computational complexity in conjugate OAM mode identification, achieved 99.53 % mode detection accuracy in the −8 to 8 range for the hybrid-turbulence of Cn2=10-14∼10-12m-2/3. Mapping and encoding a grayscale image to the OAM modes, an OAM-SK communication link was built and the demodulated bit-error-rate (BER) was only 6.85 × 10−3. We show that this approach also can effectively assist in demodulating the conventional modulation signals, promoting the communication transmission capacity. By combining OAM-SK with on–off keying (OOK) and quadrature phase shift-keying (QPSK) to construct joint-modulation links, and OOK (QPSK) signals were successfully demodulated with the BER of 8.2 × 10−9–9.1 × 10−3 (3.34 × 10−6–2.8 × 10−3) with the assistance of CNN. Our results indicate that the proposed robust CNN is not only effective for demodulating OAM-SK signals, but is also compatible with conventional modulation signals, which is very promising for OAM-SK communication applications that require OAM mode identification.

Orbital angular momentum underwater wireless optical communication system based on convolutional neural network

In this work, a novel 16-ary orbital angular momentum shift keying (OAM-SK) underwater wireless optical communication (UWOC) system based on convolutional neural network (CNN) demodulator and Gerchberg-Saxton CNN (GS-CNN) beam generator is proposed. The bit error rate (BER) performance of the proposed UWOC system with different turbulence intensity, transmission distance, and relative intensity of temperature and salinity is further investigated. By comparing with the results from the UWOC system based on GS beam generator, it is revealed that the BER performance can be improved obviously for the proposed OAM-SK UWOC system combining the CNN demodulator and GS-CNN beam generator.

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

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 transmitted over the system. Thus, in this paper, 3D chaotic interleaved multi-coded video frames (VFs) are conveyed via an N-OAM-SK free-space optical (FSO) communication system to enhance the reliability and efficiency of video communication. 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 low bit error rate (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 a trial-and-error method. The simulation results imply that LDPC-coded VFs achieve the largest 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% for the 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%.

Performance Investigation of OAMSK Modulated Wireless Optical System Over Turbulent Ocean Using Convolutional Neural Networks

The adaptive demodulators based on convolutional neural networks (CNNs) are proposed for an M-ary orbital angular momentum-shift keying (OAMSK) modulated underwater wireless optical communication (UWOC) system. The impacts of different modulation orders, oceanic turbulence strengths, seawater types, signal to noise ratios (SNRs), transmission distance, and scales of CNNs are investigated. For the proposed system model, oceanic turbulence and pass loss caused by absorption and scattering are emulated by Monte Carlo (MC) methods. By comparing four CNNs, AlexNet and GoogLeNet inception v4 are adopted as the key part of M -ary OAMSK adaptive demodulating modules. After being trained by 100 000 OAM intensity specimens under unknown levels of oceanic turbulence, the suitable network parameters are achieved. Results show that the average bit error rate performances of both CNN-based demodulators outperform the traditional conjugate demodulator by several orders of magnitude. Moreover, deeper and more complex CNN would be more effective in enhancing the ABER performance under the strong oceanic turbulence condition. Furthermore, for both CNN-based and conjugate demodulators, the ABER of the system with noise will approach the saturation values when the instantaneous SNR is approximately 26 dB larger than the pass loss. This work benefits the design of OAMSK modulation-based UWOC system.

8-ary orbital angular momentum shift keying for free-space optical communication system

The free-space optical communication based on 8-ary orbital angular momentum shift keying (8OAM-SK), where information can be encoded as the OAM state of light, is studied. We implement a numerical simulation using a phase screen model for atmospheric turbulence, which is based on the Fourier transformation method, to analyze the influence of atmospheric turbulence. The bit error rate performance of the 8OAM-SK system is investigated in several different conditions. The numerical simulations demonstrate that higher received power is required in stronger turbulence to achieve acceptable signal quality. It is also revealed that lower data rate shows better tolerance to the turbulence strength.

Comprehensive study of orbital angular momentum shift keying systems with a CNN-based image identifier

Different possible configurations of orbital angular momentum shift keying (OAM-SK) systems with an image identifier based on convolutional neural network (CNN) are comprehensively studied in this paper. These configurations transmit information by employing different OAM modes (eigenmodes, conjugate modes or multimodes) at the transmitter side and utilize coherent or incoherent detection at the receiver side. We analyze firstly the OAM mode property, the property of free-space channel with atmospheric turbulence (AT), the CNN-based image identifier, and then discuss the feasibility and performance of all possible OAM-SK configurations. We quantitatively study and compare the available OAM-SK configurations with different AT strength and transmission distances in terms of demodulation accuracy. Results show that the coherent systems outperform their incoherent counterparts in terms of the demodulation accuracy. In addition, the OAM-SK configuration with eigenmode and coherent detection has the highest demodulation accuracy among all the possible configurations.

Experimental study of machine-learning-based orbital angular momentum shift keying decoders in optical underwater channels

We experimentally demonstrate the performance of 16-ary orbital angular momentum shift keying (OAM-SK) decoders based on convolutional neural networks (CNNs) in an underwater optical wireless communication (UOWC) system. We verify the decoding accuracy of the OAM-SK system in a 1-m tank filled with flowing salty water. The oceanic turbulence is emulated by the random phase screen method by a spatial light modulator (SLM). The results show that the decoders are robust to short-distance water fluctuations induced by the water flow, with an accuracy of more than 99% in clean water. In turbid water, the decoders require input images with a greater number of pixels and can reach an accuracy of more than 99% with a pixel number of 64 × 64. The decoders are tolerant to weak turbulence, with accuracies greater than 96.78% and 99.75% for input pixel numbers of 32 × 32 and 64 × 64 in 20-m temperature-induced channels with an equivalent temperature structure parameter of Cn2=10−15K2⋅m−2∕3. As the strength of the oceanic turbulence increases, the accuracy decreases to lower than 85.5%. In both turbid channels and turbulent channels, large OAM modes are more likely to be incorrectly recognized. Increasing the pixel number of the input images or decreasing the order of the coding format can improve the decoding accuracy under moderate-to-strong turbulence conditions. This work will be beneficial for the design of UOWC systems.

Generation of Switchable Singular Beams with Dynamic Metasurfaces.

Singular beams have attracted great attention due to their optical properties and broad applications from light manipulation to optical communications. However, there has been a lack of practical schemes with which to achieve switchable singular beams with sub-wavelength resolution using ultrathin and flat optical devices. In this work, we demonstrate the generation of switchable vector and vortex beams utilizing dynamic metasurfaces at visible frequencies. The dynamic functionality of the metasurface pixels is enabled by the utilization of magnesium nanorods, which possess plasmonic reconfigurability upon hydrogenation and dehydrogenation. We show that switchable vector beams of different polarization states and switchable vortex beams of different topological charges can be implemented through simple hydrogenation and dehydrogenation of the same metasurfaces. Furthermore, we demonstrate a two-cascade metasurface scheme for holographic pattern switching, taking inspiration from orbital angular momentum-shift keying. Our work provides an additional degree of freedom to develop high-security optical elements for anti-counterfeiting applications.

Optical orbital angular momentum shift-keying communication based on coherent demodulation

Rapidly detecting orbital angular momentum (OAM) modes is crucial for optical OAM shift-keying (OAM-SK) communication. Here, we propose and investigate a novel coherent detection method for demodulating optical OAM-SK signals. A reference light beam with desired OAM modes and weights is well-designed and employed to interfere with the light beam which carries OAM-SK signals. Then the OAM-SK signals representing as the rapid switching of OAM modes are converted to intensity changes for demodulation. In the numerical simulation, a pseudo-random sequence is mapped and encoded to different OAM modes (+1, +2, +3, +4) to produce OAM-SK signals, and the demodulated bit-error-rate (BER) of 9.7656×10−4 is obtained at the signal-noise ratio (SNR) of 12-dB. By modulating the OAM modes and phase of the optical carrier synchronously, a multi-dimensional modulation format (16-ary OAM-Phase modulation) is constructed, and the demodulated BER is less than 4.8828×10−4 at the SNR of 14-dB. These results demonstrate that the coherent detection method provides an effective way to demodulate OAM-SK signals, and may shows great potential in high-speed optical OAM communications.

Coherently demodulated orbital angular momentum shift keying system using a CNN-based image identifier as demodulator

Abstract A coherently demodulated orbital angular momentum shift keying (OAM-SK) system over free-space turbulence channels using an image identifier based on convolutional neural network (CNN) as demodulator is proposed in this paper. Compared to the incoherent system, the proposed approach has the advantages of higher signal-to-ratio (SNR) of the detected image and fewer OAM topology charges employed for the same m-ary OAM-SK system. The CNN-based image identifier features in high recognition accuracy even under long free-space transmission and strong turbulence. In addition, the influence of the intensity of the local beam on the system performance, as well as the trade-off between the signal-to-noise ratio and the contrast of the received images, is fully investigated. Simulation results show that the demodulation accuracy of the proposed system with 16-ary OAM-SK system can be as high as > 99 % for 1500 m of free-space optical (FSO) transmission distance with strong atmospheric turbulence and appropriate intensity of local Gaussian beam.

Perfect optical vortex array for optical communication based on orbital angular momentum shift keying

We proposed a simple and efficient method to create a 2D perfect optical vortex (POV) array based on the Fourier shift theorem and a 2D POV array hexadecimal encoding/decoding method to realize high capacity data transmission. The encoding data sequence contains the position and topological charge information of each POV, which can be modulated by adding a phase shift factor to a specially designed hybrid phase-only grating. Based on the position and topological charge information, the 2D POV array can realize spatial division multiplexing and orbital angular momentum shift keying. The hexadecimal encoding/decoding method was investigated both theoretically and experimentally. The experimental results show that this method has potential applications in high capacity optical communications.

Analysis of an adaptive orbital angular momentum shift keying decoder based on machine learning under oceanic turbulence channels

Abstract Oceanic turbulence tends to degrade the performance of underwater optical communication (UOC) systems based on orbital angular momentum (OAM) shift keying (SK). A decoder for the UOC-OAM-SK using convolutional neural networks (CNNs) is investigated. We simulate 8 kinds of superposition Laguerre-Gaussian (LG) beams as a trinary OAM-SK encoder; these beams propagate under simulated oceanic channels. The results show that in temperature-dominated situations, the decoders based on the CNN have a high accuracy (nearly 100%) under weak-to-moderate turbulence and have an accuracy greater than 93% under strong turbulence at a distance of 60 m. Under weak-to-moderate turbulence, the accuracies are higher than 95% within 80 m, and under strong turbulence, the accuracies are lower than 90% after 60 m propagation. The decoder with an incorporated CNN is insensitive to the balance parameter in most situations, except for those that are salinity dominated. Furthermore, the CNN trained with a database mixed with several levels of turbulence has a higher accuracy when accommodating an unknown level of turbulence than when trained with a single level of turbulence. This work is expected to aid in the future design of UOC-OAM-SK systems.

Performance Investigation of Underwater Wireless Optical Communication System Using M -ary OAMSK Modulation Over Oceanic Turbulence

The performance of M -ary orbital angular momentum-shift keying (OAMSK) modulation-based underwater wireless optical communication (UWOC) system is investigated over oceanic turbulence with Laguerre–Gauss beam considered. On the basis of Rytov approximation, the detection probability and power distribution among the received signals are derived. The conditional probability and symbol error rate (SER) are, then, achieved by the maximum likelihood estimation. With Blahut–Arimoto algorithm, the channel capacity for this UWOC system is obtained. The results show that the optimal transmitted OAM mode set S is mainly restricted by the interfering energy and decaying effective energy. With the optimal mode interval achieved, the SER performances for different modulation orders M and the minimum required transmitting power to reach the SER of $P_{e} = $ 10−9 are sensitive to the variation in oceanic turbulence conditions. Additionally, the value of capacity will shift to a higher value with the increasing M , and a suitable M should be selected to balance the transmitting power consumption and capacity gain. This work is beneficial to M -ary OAMSK modulation-based UWOC system design.

Orbital Angular Momentum Shift Keying Based Optical Communication System

In the free space optical communication, the information can be encoded as the orbital angular momentum (OAM) state of light, which is called OAM shift keying (OAM-SK). This paper has proposed a communication system with OAM-SK, in which an image has been delivered from the transmitter to the receiver successfully in the simulation environment. Specifically, we have carefully designed and implemented the phase holograms used at the transmitter and the receiver for multiplexing and de-multiplexing the OAM states, respectively. At the transmitter, the multiplexing phase hologram designed by the modified Lin's algorithm is loaded on the spatial light modulator 1 (SLM1) to generate the multiplexing vortex beam, which is a superposition of multiple vortex beams with different OAM states. Correspondingly, at the receiver, a novel phase hologram is designed and loaded on the SLM2 to effectively de-multiplex the multiplexing vortex beam in different directions. In our phase hologram used at the receiver, the detected power of each OAM state can be controlled by adjusting the weight coefficient by the modified Lin's algorithm. This way, the incident power can be concentrated to the target OAM states, from which the target OAM states can be detected more effectively than conventional fork grating.