Existing wavefront sensorless (WFS-less) adaptive optics (AO) generally require a search algorithm that takes lots of iterations and measurements to get optimal results. So the latency is a serious problem in the current WFS-less AO system, especially in applications to free-space optics communication. To solve this issue, we propose a deep neural network (DNN)–based aberration correction method. The DNN model can detect the wavefront distortion directly from the intensity images, thereby avoiding time-consuming iterative processes. Since the tip-and-tilt mode of Zernike coefficients are considered, the tip-tilt correction system is not necessarily required in the proposed method. From our simulation results, the proposed method can effectively reduce the computation time and has an impressive improvement of root mean square (RMS) in different turbulence conditions.

In this paper, a novel turbo-coded 16-ary orbital angular momentum - shift keying-free space optical (OAM-SK-FSO) communication system combining a convolutional neural network (CNN) based adaptive demodulator under strong atmospheric turbulence is proposed for the first time. The feasibility of the scheme is verified by transmitting a 256-grayscale two-dimensional digital image. The bit error ratio (BER) performance of the system is investigated and the effect of different factors such as turbulence strength, propagation distance, code rate, length of random interleaver and length of bit interleaver is also taken into account. An advanced encoder/decoder structure and mapping scheme are applied to diminish the influence of CNN misclassification and reduce the BER effectively. With the optimal encoder/decoder structure and CNN model settings, the BER varies from 0 to $4.89\times {10}^{-4}$ when the propagation distance increases from 200m to 1000m for a given turbulence strength ${\text{C}}_n^2$ equals $5\times {10}^{-14}{m}^{-2/3}$. For a determined propagation distance equals 400m, the BER ranges from 0 to $4.01\times {10}^{-4}$ when ${\text{C}}_n^2$increases from $1\times {10}^{-15}{m}^{-2/3}$ to $4\times {10}^{-13}{m}^{-2/3}$. Our numerical simulations demonstrate that the proposed system can provide better BER performance under strong atmospheric turbulence and conditions when the classification ability of CNN is limited.

Orbital angular momentum (OAM) has the potential to dramatically enhance the amount of information in the Internet of Underwater Things (IoUT) system. Nevertheless, underwater-turbulence-induced scintillation will destroy the orthogonality of OAM modes, hence degrading the performance of the system. In this article, a random-amplitude-mask-based adaptive optics (AOs) technique is proposed for the sake of mitigating the turbulence effects in the OAM-based underwater wireless optical communication (UWOC) system. Combined with phase retrieval algorithms, the magnitudes of linear measurements obtained from the distorted OAM beams modulated with a series of random amplitude masks and focused by a lens are employed for the phase estimation. Furthermore, we present a comprehensive performance comparison against state-of-the-art phaseless wave-front sensing techniques. Moreover, the mixture exponential-generalized gamma (EGG) distribution is applied for characterizing the probability density function (PDF) of reference-channel irradiance of OAM beams coupled into a single-mode fiber (SMF). In the end, the performance metrics, such as the outage probability, the average bit-error-rate (BER), and the ergodic capacity are analyzed with the aid of PDF for both single-input-single-output (SISO) and multiinput-multioutput (MIMO) systems. In a nutshell, this article provides new insights for the applications of AO in the OAM-based UWOC system, which can serve as a candidate for supporting IoUT devices.

Predicting the traffic in metro and access networks has a significant influence on expanding and upgrading the network. However, the intricate temporal and spatial correlations in such networks are affected by the users' behaviour and geographic characteristics. Consequently, predicting network traffic in metro or access networks faces significant challenges. To enhance the accuracy of this prediction, this paper explores a spatio-temporal approach to predicting traffic, based on a transformer model. This model effectively captures the temporal correlations in the traffic through a multi-head attention mechanism, learning long-term correlations resulting from the behaviour of the users of the individual nodes. Additionally, it acquires the spatial correlations of the traffic through a graph convolutional layer to capture short-term bursty correlations between users. Unlike previous single-point predictions, we optimize the model to make collaborative predictions for all the network nodes. Through parameter optimization, the improved model accurately predicts network traffic in small-scale regions. We implemented simulations using OMNET++ and predicted the traffic in various scenarios. Concurrently, we validated the proposed method with real network traffic based on operator measurements, achieving a prediction accuracy of 98.49%.

In this paper, a temperature-compensated three-dimension vector fiber optic magnetic field sensor based on an elliptical core micro fiber Bragg grating (FBG) has been proposed and experimentally demonstrated. The elliptical core fiber was tapered to form a microfiber, in which a FBG was inscribed. Due to the magnetism-manipulation of the anisotropic aggregation of ferromagnetism nanoparticles around the fiber surface, the effective refractive index of the evanescent field for two orthogonal polarization modes was modulated, and the magnetic field orientation can be detected by interrogating the wavelength interval between two reflection peaks. However, two reflection peaks show the identical response to ambient temperature. Hence the proposed sensor can achieve the measurements of the magnetic field intensity and the orientation simultaneously without the temperature cross-sensitivity. The experimental results show that the magnetic field orientation sensitivity of 15 pm/deg and intensity sensitivity of 81 pm/mT can be achieved, and the maximum standard variation of the temperature cross-sensitivity is only 0.02 nm. The proposed elliptical core micro FBG appears to have potential applications in navigation, vehicle detection, and current sensing.

Stochastic nonlinear impairment is the primary factor that limits the transmission performance of high-speed orbital angular momentum (OAM) mode-division multiplexing (MDM) optical fiber communication systems. This Letter presents a low-complexity adaptive-network-based fuzzy inference system (LANFIS) nonlinear equalizer for OAM-MDM intensity-modulation direct-detection (IM/DD) transmission with three OAM modes and 15 wavelength division multiplex (WDM) channels. The LANFIS equalizer could adjust the probability distribution functions (PDFs) of the distorted pulse amplitude modulation (PAM) symbols to fit the statistical characteristics of the WDM–OAM-MDM transmission channel. Therefore, although the transmission symbols in the WDM–OAM-MDM system are subjected to a stochastic nonlinear impairment, the proposed LANFIS equalizer can effectively compensate the distorted signals. The proposed equalizer outperforms the Volterra equalizer with improvements in receiver sensitivity of 2, 1.5, and 1.3 dB for three OAM modes at a wavelength of 1550.12 nm, respectively. It also outperforms a CNN equalizer, with improvements in receiver sensitivity of 1, 0.5, and 0.3 dB, respectively. Moreover, complexity reductions of 67%, 74%, and 99.9% are achieved for the LANFIS equalizer compared with the Volterra, CNN, and ANFIS equalizers, respectively. The proposed equalizer has high performance and low complexity, making it a promising candidate for a high-speed WDM–OAM-MDM system.