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

Autonomous Self-Adaptive and Self-Aware Optical Wireless Communication Systems

The future age of optical networks demands autonomous functions to optimize available resources. With autonomy, the communication network should be able to learn and adapt to the dynamic environment. Among the different autonomous tasks, this work considers building self-adaptive and self-awareness-free space optic (FSO) networks by exploiting advances in artificial intelligence. In this regard, we study the use of machine learning (ML) techniques to build self-adaptive and self-awareness FSO systems capable of classifying the modulation format/baud rate and predicting the number of channel impairments. The study considers four modulation formats and four baud rates applicable in current commercial FSO systems. Moreover, two main channel impairments are considered. The results show that the proposed ML algorithm is capable of achieving 100% classification accuracy for the considered modulation formats/baud rates even under harsh channel conditions. Moreover, the prediction accuracy of the channel impairments ranges between 71% and 100% depending on the predicted parameter type and channel conditions.

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.

Classifying beams carrying orbital angular momentum with machine learning: tutorial

This tutorial discusses optical communication systems that propagate light carrying orbital angular momentum through random media and use machine learning (aka artificial intelligence) to classify the distorted images of the received alphabet symbols. We assume the reader is familiar with either optics or machine learning but is likely not an expert in both. We review select works on machine learning applications in various optics areas with a focus on beams that carry orbital angular momentum. We then discuss optical experimental design, including generating Laguerre-Gaussian beams, creating and characterizing optical turbulence, and engineering considerations when capturing the images at the receiver. We then provide an accessible primer on convolutional neural networks, a machine learning technique that has proved effective at image classification. We conclude with a set of best practices for the field and provide an example code and a benchmark dataset for researchers looking to try out these techniques.

Towards fine recognition of orbital angular momentum modes through smoke

Light beams carrying orbital angular momentum (OAM) have been constantly developing in free-space optical (FSO) communications. However, perturbations in the free space link, such as rain, fog, and atmospheric turbulence, may affect the transmission efficiency of this technique. If the FSO communications procedure takes place in a smoke condition with low visibility, the communication efficiency also will be worse. Here, we use deep learning methods to recognize OAM eigenstates and superposition states in a thick smoke condition. In a smoke transmission link with visibility about 5 m to 6 m, the experimental recognition accuracy reaches 99.73% and 99.21% for OAM eigenstates and superposition states whose Bures distance is 0.05. Two 6 bit/pixel pictures were also successfully transmitted in the extreme smoke conditions. This work offers a robust and generalized proposal for FSO communications based on OAM modes and allows an increase of the communication capacity under the low visibility smoke conditions.

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.

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.

Free space optic channel monitoring using machine learning

Free space optic (FSO) is a type of optical communication where the signal is transmitted in free space instead of fiber cables. Because of this, the signal is subject to different types of impairments that affect its quality. Predicting these impairments help in automatic system diagnosis and building adaptive optical networks. Using machine learning for predicting the signal impairments in optical networks has been extensively covered during the past few years. However, for FSO links, the work is still in its infancy. In this paper, we consider predicting three channel parameters in FSO links that are related to amplified spontaneous emission (ASE) noise, turbulence, and pointing errors. To the best of authors knowledge, this work is the first to consider predicting FSO channel parameters under the effect of more than one impairment. First, we report the performance of predicting the FSO parameters using asynchronous amplitude histogram (AAH) and asynchronous delay-tap sampling (ADTS) histogram features. The results show that ADTS histogram features provide better prediction accuracy. Second, we compare the performance of support vector machine (SVM) regressor and convolutional neural network (CNN) regressor using ADTS histogram features. The results show that CNN regressor outperforms SVM regressor for some cases, while for other cases they have similar performance. Finally, we investigate the capability of CNN regressor for predicting the channel parameters for three different transmission speeds. The results show that the CNN regressor has good performance for predicting the OSNR parameter regardless of the value of transmission speed. However, for the turbulence and pointing errors, the prediction under low speed transmission is more accurate than under high speed transmission.

OAM light propagation through tissue

A major challenge in use of the optical spectrum for communication and imaging applications is the scattering of light as it passes through diffuse media. Recent studies indicate that light beams with orbital angular momentum (OAM) can penetrate deeper through diffuse media than simple Gaussian beams. To the best knowledge of the authors, in this paper we describe for the first time an experiment examining transmission of OAM beams through biological tissue with thickness of up to a few centimeters, and for OAM modes reaching up to 20. Our results indicate that OAM beams do indeed show a higher transmittance relative to Gaussian beams, and that the greater the OAM, the higher the transmittance also up to 20, Our results extend measured results to highly multi scattering media and indicate that at 2.6 cm tissue thickness for OAM of order 20, we measure nearly 30% more power in comparison to a Gaussian beam. In addition, we develop a mathematical model describing the improved permeability. This work shows that OAM beams can be a valuable contribution to optical wireless communication (OWC) for medical implants, optical biological imaging, as well as recent innovative applications of medical diagnosis.

Patent Technology Network Analysis of Machine-Learning Technologies and Applications in Optical Communications

As the Internet of Things (IoT) develops, applying machine learning on optical communications has become a prospective field of research. Scholars have mostly concentrated on algorithmic techniques or specific applications but have been unable to address the distribution of machine-learning technologies and the development of its applications in optical communications from a macro perspective. Therefore, in this paper, machine-learning patents in optical communications are taken as the analytical basis for constructing a patent technology network. The study results revealed that key technologies were primarily in data input and output devices, data-processing methods, wireless communication networks, and the transmission of digital information in optical communications. Such technologies were also applied to perform measurement for diagnostic purposes and medical diagnoses. The technology network model proposed in this paper explores the technological development trends of machine learning in optical communications and serves as a reference for allocating research and development resources.