Machine learning-based classification of structured light modes under turbulence and eavesdropping effects.

This paper considers the classification of multiplexed structured light modes, aiming to bolster communication reliability and data transfer rates, particularly in challenging scenarios marked by turbulence and potential eavesdropping. An experimental free-space optic (FSO) system is established to transmit 16 modes [8-ary Laguerre Gaussian (LG) and 8-ary superposition LG (Mux-LG) mode patterns] over a 3-m FSO channel, accounting for interception threats and turbulence effects. To the best of authors' knowledge, this paper is the first to consider both factors concurrently. We propose four machine/deep learning algorithms-artificial neural network, support vector machine, 1D convolutional neural network, and 2D convolutional neural network-for classification purposes. By fusing the outputs of these methods, we achieve promising classification results exceeding 92%, 81%, and 69% in cases of weak, moderate, and strong turbulence, respectively. Structured light modes exhibit significant potential for a variety of real-world applications where reliable and high-capacity data transmission is crucial.

Multievent localization for loop-based Sagnac sensing system using machine learning.

In optical sensing applications such as pipeline monitoring and intrusion detection systems, accurate localization of the event is crucial for timely and effective response. This paper experimentally demonstrates multievent localization for long perimeter monitoring using a Sagnac interferometer loop sensor and machine learning techniques. The proposed method considers the multievent localization problem as a multilabel multiclassification problem by dividing the optical fiber into 250 segments. A deep neural network (DNN) model is used to predict the likelihood of event occurrence in each segment and accurately locate the events. The sensing loop comprises 106.245 km of single-mode fiber, equivalent to ∼50 km of effective sensing distance. The training dataset is constructed in simulation using VPItransmissionMaker, and the proposed machine learning model's complexity is reduced by using discrete cosine transform (DCT). The designed DNN is tested for event localization in both simulation and experiment. The simulation results show that the proposed model achieves an accuracy of 99% in predicting the location of one event within one segment error, an accuracy of 95% in predicting the location of one event out of the two within one segment error, and an accuracy of 78% in predicting the location of the two events within one segment error. The experimental results validate the simulation ones, demonstrating the proposed model's effectiveness in accurately localizing events with high precision. In addition, the paper includes a discussion on extending the proposed model to sense more than two events simultaneously.

Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding.

This paper demonstrates an intruder detection system using a strain-based optical fiber Bragg grating (FBG), machine learning (ML), and adaptive thresholding to classify the intruder as no intruder, intruder, or wind at low levels of signal-to-noise ratio. We demonstrate the intruder detection system using a portion of a real fence manufactured and installed around one of the engineering college's gardens at King Saud University. The experimental results show that adaptive thresholding can help improve the performance of machine learning classifiers, such as linear discriminant analysis (LDA) or logistic regression algorithms in identifying an intruder's existence at low optical signal-to-noise ratio (OSNR) scenarios. The proposed method can achieve an average accuracy of 99.17% when the OSNR level is <0.5 dB.

Performance investigation of an ambiguity function-shaped waveform (AFSW) using a photonics-based radar system.

In this work, we investigate the performance of an ambiguity function-shaped waveform (AFSW) using a millimeter-wave photonics-based radar system at 100 GHz. An AFSW is a radar waveform whose ambiguity function can be shaped to increase the peak-to-sidelobe ratio (PSR) for better detectability of targets in a desired range/velocity region. To the best of the authors' knowledge, this paper is the first in the literature that investigates the performance of such a waveform in a photonics-based radar system. We experimentally compare the AFSW performance to the conventional frequency-modulated continuous wave (FMCW). The experimental results show the ability of the AFSW to achieve a PSR of 38.35 dB compared to the PSR of 14.5 dB obtained using the conventional FMCW. Moreover, we investigate the effects of some optical system impairments on the AFSW, such as: (i) optical modulator nonlinearity, (ii) optical modulator bias drift, and (iii) sampling offset error between the transmitter and receiver.

Discriminative strain and temperature sensing using a ring-hyperbolic tangent fiber sensor.

Brillouin fiber sensors have demonstrated strong capability in discriminative and high-sensitivity multiparameter measurements. In this study, we proposed and numerically investigated novel ring core fiber-based stimulated Brillouin scattering for the simultaneous measurement of temperature and strain. The novel fiber, referred to as ring hyperbolic tangent (R-HTAN) fiber, is characterized by a shape parameter (α) that controls the optical refractive index and longitudinal acoustic velocity profiles. Numerical modal simulations indicated that the Brillouin gain spectrum contained multiple widely spaced and high-gain peaks, which were attributed to the strong interaction between the optical linearly polarized mode (i.e., LP0,1 as a pump wave) and multiple high-order longitudinal acoustic modes. The designed R-HTAN fiber enabled the discriminative sensing of temperature and strain with levels that clearly surpassed values recently reported in the literature. In case of straight R-HTAN fiber (α = 0), the maximum C(α=0)T and C(α=0)ε are 1.928 MHz/ ∘C and 0.087 MHz, respectively. In case of graded R-HTAN fiber (α = 1), the maximum C(α=1)T and C(α=1)ε are 1.872 MHz/ ∘C and 0.0842 MHz/µÉ›, respectively. The errors associated with temperature measurements (maximum δT(α=0) = 0.0846 ∘C and maximum δT(α=1) = 7.4184 ∘C) and strain measurements (maximum δɛ(α=0) = 0.7250 µÉ› and maximum δɛ(α=1) = 7.4184 µÉ›) demonstrated that the proposed fiber could be a promising candidate for next-generation Brillouin sensing systems for enabling temperature and strain discrimination.

Reconfigurable photonics-based millimeter wave signal aggregation for non-orthogonal multiple access.

A reconfigurable optical-to-electrical signal aggregation is proposed, for the first time, using optical signal processing and photo-mixing technology. Two optically modulated quadrature phase-shift keying (QPSK) signals are aggregated into a single 16-quadrature amplitude modulation (16-QAM) signal and, simultaneously, carried over a 28-GHz millimeter wave (MMW) carrier using an optimized heterodyne beating process through a single photodiode. To demonstrate the system reconfigurability, aggregation of two optical binary phase-shift keying signals is mapped into MMW QPSK or four-level pulse amplitude modulation signals by controlling the relative phases and amplitudes, respectively, of the input signals. In addition, the aggregation of two 16-QAM signals into a 256-QAM signal and the aggregation of three QPSK signals into a 64-QAM format are achieved. Besides, we report the effect of laser phase noise on signal aggregation performance. The de-aggregation of the aggregated MMW signals is performed electrically using a successive interference cancellation algorithm. Moreover, a proof-of-concept experiment is conducted to validate the numerical simulations. Two 1-Gbaud BPSK (1 Gbps) and QPSK (2 Gbps) optical signals are optically transmitted over a 20-km single-mode fiber as MMW over fiber signals. Then, the signals are aggregated into QPSK (2 Gbps) and 16-QAM (4 Gbps) 28-GHz MMW signals, respectively. The aggregated signal is further transmitted over a 1-m wireless channel. The performance of the proposed system is evaluated using bit error rate and error vector magnitude metrics.

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.

Modulation format identification of optical signals: an approach based on singular value decomposition of Stokes space projections.

In this paper, two Stokes space (SS) analysis schemes for modulation format identification (MFI) are proposed. These schemes are based on singular value decomposition (SVD) and Radon transform (RT) for feature extraction. The singular values (SVs) are extracted from the SS projections for different modulation formats to discriminate between them. The SS projections are obtained at different optical signal-to-noise ratios (OSNRs) ranging from 11 to 30 dB for seven dual-polarized modulation formats. The first scheme depends on the SVDs of the SS projections on three planes, while the second scheme depends on the SVDs of the RTs of the SS projections. Different classifiers including support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN) for MFI based on the obtained features are used. Both simulation and experimental setups are arranged and tested for proof of concept of the proposed schemes for the MFI task. Complexity reduction is studied for the SVD scheme by applying the decimation of the projections by two and four to achieve an acceptable classification rate, while reducing the computation time. Also, the effect of the variation of phase noise (PN) and state of polarization (SoP) on the accuracy of the MFI is considered at all OSNRs. The two proposed schemes are capable of identifying the polarization multiplexed modulation formats blindly with high accuracy levels up to 98%, even at low OSNR values of 12 dB, high PN levels up to 10 MHz, and SoP up to 45°.

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

The unique orthogonal shapes of structured light beams have attracted researchers to use as information carriers. Structured light-based free space optical communication is subject to atmospheric propagation effects such as rain, fog, and rain, which complicate the mode demultiplexing process using conventional technology. In this context, we experimentally investigate the detection of Laguerre Gaussian and Hermite Gaussian beams under dust storm conditions using machine learning algorithms. Different algorithms are employed to detect various structured light encoding schemes including the use of a convolutional neural network (CNN), support vector machine, and k-nearest neighbor. We report an identification accuracy of 99% under a visibility level of 9 m. The CNN approach is further used to estimate the visibility range of a dusty communication channel.