768-ary Laguerre-Gaussian-mode shift keying free-space optical communication based on convolutional neural networks

High-order orbital angular momentum mode-based phase shift-keying communication using phase difference modulation

Orbital angular momentum (OAM) mode offers a promising modulation dimension for high-order shift-keying (SK) communication due to its mode orthogonality. However, the expansion of modulation order through superposing OAM modes is constrained by the mode-field mismatch resulting from the rapidly increased divergence with mode orders. Herein, we address this problem by propose a phase-difference modulation strategy that breaks the limitation of modulation orders via introducing a phase-difference degree of freedom (DoF) beyond OAM modes. Phase-difference modulation exploits the sensitivity of mode interference to phase differences, thereby providing distinct tunable parameters. This enables the generation of a series of codable spatial modes with continuous variation within the same superposed OAM modes by manipulating the interference state. Due to the inherent independence between OAM mode and phase-difference DoF, the number of codable modes increases exponentially, which facilitates establishing ultra-high-order phase shift-keying by discretizing the continuous phase difference and establishing a one-to-one mapping between coding symbols and constructed modes. We show that a phase shift-keying communication link with a modulation order of up to 4 × 104 is achieved by employing only 3 OAM modes (+1, + 2 and +3), and the decode accuracy reaches 99.9%. Since the modulation order is exponentially correlated with the OAM modes and phase differences, the order can be greatly improved by further increasing the superimposed OAM modes, which may provide new insight for high-order OAM-based SK communication.

1024-ary composite OAM shift keying for free-space optical communication system decoded by a two-step neural network

The demand for high-dimensional encoding techniques for communication systems is increasing. Vortex beams carrying orbital angular momentum (OAM) provide new degrees of freedom for optical communication. In this study, we propose an approach for increasing the channel capacity of free-space optical communication systems by integrating superimposed orbital angular momentum (OAM) states and deep learning techniques. We generate composite vortex beams with topological charges ranging from -4 to 8 and radial coefficients ranging from 0 to 3. A phase difference among each OAM state is introduced to significantly increase the number of available superimposed states, achieving up to 1024-ary codes with distinct features. To accurately decode the high-dimensional codes, we propose a two-step convolutional neural network (CNN). The first step is to make a coarse classification of the codes, while the second step is to finely identify the code and achieve decoding. Our proposed method demonstrates 100% accuracy achieved for the coarse classification after 7 epochs, 100% accuracy achieved for the fine identification after 12 epochs, and 99.84% accuracy achieved for testing, which is much faster and more accurate than one-step decoding. To demonstrate the feasibility of our method, we successfully transmitted a 24-bit true-color Peppers image once with a resolution of 64 × 64 in the laboratory, yielding a bit error rate of 0.

65,536-ary orbital angular momentum-shift keying free-space optical communication based on few-shot learning

In an orbital angular momentum-shift keying free-space optical (OAM-SK FSO) communication system, precisely recognizing OAM superposed modes at the receiver site is crucial to improve the communication capacity. While deep learning (DL) provides an effective method for OAM demodulation, with the increase of OAM modes, the dimension explosion of OAM superstates results in unacceptable costs on training the DL model. Here, we demonstrate a few-shot-learning-based demodulator to achieve a 65,536-ary OAM-SK FSO communication system. By learning from only 256 classes of samples, the remaining 65,280 unseen classes can be predicted with an accuracy of more than 94%, which saves a large number of resources on data preparation and model training. Based on this demodulator, we first realize the single transmission of a color pixel and the single transmission of two gray scale pixels on the application of colorful-image-transmission in free space with an average error rate less than 0.023%. This work may provide a new, to the best of our knowledge, approach for big data capacity in optical communication systems.

The mr-MDA: An Invariant to Shifting, Scaling, and Rotating Variance for 3D Object Recognition Using Diffractive Deep Neural Network

The diffractive deep neural network (D²NN) can efficiently accomplish 2D object recognition based on rapid optical manipulation. Moreover, the multiple-view D²NN array (MDA) possesses the obvious advantage of being able to effectively achieve 3D object classification. At present, 3D target recognition should be performed in a high-speed and dynamic way. It should be invariant to the typical shifting, scaling, and rotating variance of targets in relatively complicated circumstances, which remains a shortcoming of optical neural network architectures. In order to efficiently recognize 3D targets based on the developed D²NN, a more robust MDA (mr-MDA) is proposed in this paper. Through utilizing a new training strategy to tackle several random disturbances introduced into the optical neural network system, a trained mr-MDA model constructed by us was numerically verified, demonstrating that the training strategy is able to dynamically recognize 3D objects in a relatively stable way.

Deep-learning-assisted communication capacity enhancement by non-orthogonal state recognition of structured light

In light of pending capacity crunch in information era, orbital-angular-momenta-carrying vortex beams are gaining traction thanks to enlarged transmission capability. However, high-order beams are confronted with fundamental limits of nontrivial divergence or distortion, which consequently intensifies research on new optical states like low-order fractional vortex beams. Here, we experimentally demonstrate an alternative mean to increase the capacity by simultaneously utilizing multiple non-orthogonal states of structured light, challenging a prevailing view of using orthogonal states as information carriers. Specifically, six categories of beams are jointly recognized with accuracy of >99% by harnessing an adapted deep neural network, thus providing the targeted wide bandwidth. We then manifest the efficiency by sending/receiving a grayscale image in 256-ary mode encoding and shift keying schemes, respectively. Moreover, the well-trained model is able to realize high fidelity recognition (accuracy >0.8) onto structured beams under unknown turbulence and restricted receiver aperture size. To gain insights of the framework, we further interpret the network by revealing the contributions of intensity signals from different positions. This work holds potential in intelligence-assisted large-capacity and secure communications, meeting ever growing demand of daily information bandwidth.

Dual Polarization Simultaneous Optical Intensity Modulation in Single Birefringent LiNbO3 Mach–Zehnder Optical Modulator

A new method of simultaneous modulation of dual-polarization intensity based on a single LiNbO3 Mach–Zehnder modulator (MZM) is proposed. Using the birefringence of lithium niobate, the bipolar optical intensity can be modulated independently with the proposed modulation condition. In this paper, the optimized modulation conditions and the performance of the proposed method are investigated through simulations and experiments.

Structured Light Transmission under Free Space Jamming: An Enhanced Mode Identification and Signal-to-Jamming Ratio Estimation Using Machine Learning

In this paper, we develop new classification and estimation algorithms in the context of free space optics (FSO) transmission. Firstly, a new classification algorithm is proposed to address efficiently the problem of identifying structured light modes under jamming effect. The proposed method exploits support vector machine (SVM) and the histogram of oriented gradients algorithm for the classification task within a specific range of signal-to-jamming ratio (SJR). The SVM model is trained and tested using experimental data generated using different modes of the structured light beam, including the 8-ary Laguerre Gaussian (LG), 8-ary superposition-LG, and 16-ary Hermite Gaussian (HG) formats. Secondly, a new algorithm is proposed using neural networks for the sake of predicting the value of SJR with promising results within the investigated range of values between −5 dB and 3 dB.

Multidimensional phase singularities in nanophotonics

[Figure: see text].