TY - JOUR
T1 - Atmospheric turbulence forecasting using two-stage variational mode decomposition and autoregression towards free-space optical data-transmission link
AU - Li, Yalin
AU - Li, Lang
AU - Guo, Yingchi
AU - Zhang, Hongqun
AU - Fu, Shiyao
AU - Gao, Chunqing
AU - Yin, Ci
N1 - Publisher Copyright:
Copyright © 2022 Li, Li, Guo, Zhang, Fu, Gao and Yin.
PY - 2022/8/8
Y1 - 2022/8/8
N2 - 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.
AB - 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.
KW - autoregression model
KW - correlation analysis
KW - free-space optical communication
KW - turbulence forecasting
KW - two-stage variational mode decomposition
UR - http://www.scopus.com/inward/record.url?scp=85136468681&partnerID=8YFLogxK
U2 - 10.3389/fphy.2022.970025
DO - 10.3389/fphy.2022.970025
M3 - Article
AN - SCOPUS:85136468681
SN - 2296-424X
VL - 10
JO - Frontiers in Physics
JF - Frontiers in Physics
M1 - 970025
ER -