Atmospheric turbulence forecasting using two-stage variational mode decomposition and autoregression towards free-space optical data-transmission link

Abstract

Free space optical communication (FSOC) is a promising technology for satellite-to-earth communication systems, where vector beams, especially orbital angular momentum (OAM), can further increase the capacity of the optical link. However, atmospheric turbulence along the path can introduce intensity scintillation, wavefront aberrations and severe distortion of spatial patterns, leading to data degradation. Forecasting atmospheric turbulence allows for advanced scheduling of satellite-to-earth data transmission links, as well as the use of adaptive optics (AO) to compensate for turbulence effects and avoid data transmission link performance degradation. Therefore, atmospheric turbulence forecasting is critical for practical applications. In this work, we proposed a hybrid atmospheric turbulence forecasting model based on a two-stage variational mode decomposition (TsVMD) and autoregression model. The variational mode decomposition (VMD) algorithm is first used, to our best knowledge, to denoise the observed atmospheric turbulence dataset, and then is used again to decompose the datasets into several intrinsic mode functions (IMFs). Finally, the autoregression model is used to predict each IMF independently. And the predictions of each IMF are combined to obtain the final atmospheric turbulence predictions. Experiments employing the observed turbulence datasets and two additional methodologies were carried out to verify the performance of the proposed model. The experimental results show that the performance of the proposed model is much superior to that of the comparative methods.

Average Intensity of a Laguerre—Gaussian Vector Vortex Beam through Inhomogeneous Atmospheric Turbulence

“…where r = (x, y) is an arbitrary transverse position vector in the initial plane, w 0 represents the beam waist for the fundamental Gaussian beam, m and n denote the topological charge number and the radial index, respectively, and e x and e y are the unit vectors in the x and y directions, respectively. H 2t+m−s (•) and H 2n−2t+s (•) are the Hermitian polynomials; we use their summation form to represent the Laguerre term in the original LGVV beam expression [14,15,[26][27][28][29][30]. And n t and m s are the binomial coefficients.…”

Multistep ahead atmospheric optical turbulence forecasting for free-space optical communication using empirical mode decomposition and LSTM-based sequence-to-sequence learning

“…Typical autoregressive models include the autoregressive integrated moving average (ARIMA) approach and the filter method. The ARIMA model is trained on a huge quantity of historical data to simulate the temporal dependency of atmospheric optical turbulence, and then the historical data are used to predict short-term atmospheric optical turbulence [29]. The filter method takes into account, simultaneously, the forecast obtained with non-hydrostatical mesoscale atmospheric models and the real-time measurements to help in removing potential biases and trends which have an impact on short-term atmospheric optical turbulence forecasting [30,31].…”

Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm

“…Wavefront sensorless sensing technology, as compared to adaptive optics systems with wavefront sensing, not only reduces the complexity and cost of adaptive optics systems, but it also has significant advantages in situations with weak light or a large turbulence amplitude with distant targets. As a result, they have attracted widespread attention for laser technology and laser communication applications [1][2][3][4][5][6][7] and are one of the most investigated topics in adaptive optics research [8][9][10][11][12].…”