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Engineering Group Mini Review 記事ID: igmin230

Analysis of Reliable Transmission Performance Optimization Methods for Satellite-to-Ground Laser Communication Links

Electronics DOI10.61927/igmin230 Affiliation

Affiliation

    1Changchun University of Science and Technology, Changchun, Jilin, China

    2Nanjing University, Nanjing, Jiangsu, China

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要約

The satellite-to-ground laser communication link plays a crucial role in addressing the technical bottleneck of high-speed information transmission between satellite and ground networks. However, the impact of atmospheric channel conditions poses challenges to the link performance. To overcome these challenges, this paper presents a comprehensive analysis of the characteristics and influencing factors of the satellite-to-ground laser communication link. It summarizes methods for optimizing the transmission performance of the link and proposes approaches for optimizing, monitoring, and predicting the link performance through factors such as the optimization of ground station quantity and location, link availability, link transmission quality, and adaptive coding and modulation. These methods enable the optimization of the transmission performance of the satellite-to-ground laser communication link, thus significantly contributing to the realization of high-speed and reliable laser communication between satellite and ground networks.

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参考文献

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    3. Schieler CM, Riesing KM, Bilyeu BC, et al. On-orbit demonstration of 200-Gbps laser communication downlink from the TBIRD CubeSat. Free-Space Laser Communications XXXV. SPIE. 2023;12413:1241302.
    4. Fu W. Analysis of Optical Satellite Communication Technology and Its Development Trend. SHS Web Conf. 2022;144:02013.
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    6. Erdogan E, Altunbas I, Kurt GK, et al. Site diversity in downlink optical satellite networks through ground station selection. IEEE Access. 2021;9:31179-31190.
    7. Jiang X, Zhao S, Li Y, et al. Research on Spatial Diversity Technology for Ground Stations in Satellite-Ground Optical Communication. J Appl Optics. 2012;33(01):229-232+174.
    8. Dai Z. Cloud Cluster Change Trend Prediction System Based on Deep Learning. Nanjing Univ. 2022.
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    10. Hou X, Hu Y, Du F, et al. Machine learning-based seeing estimation and prediction using multi-layer meteorological data at Dome A, Antarctica. Astron Comput. 2023;43:100710.
    11. Wang L, Hao S, Zhang D, et al. Mode Selection Threshold for Adaptive Modulation Coding Technology in Atmospheric Laser Communication. Laser Optoelectron Prog. 2017;54(02):117-123.
    12. Giordano C, Ziad A, Aristidi E, et al. The upgraded Calern Atmospheric Turbulence Station. Adaptive Opt Syst VIII. SPIE. 2022;12185:1868-1876.
    13. Wang Y, Basu S. Using an artificial neural network approach to estimate surface-layer optical turbulence at Mauna Loa, Hawaii. Opt Lett. 2016;41(10):2334-2337.
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    15. Gu Y. Research on Modulation Format Identification Technology of Satellite-Ground Laser Communication System. Univ Chinese Acad Sci (Changchun Inst Opt Fine Mech Phys, Chin Acad Sci). 2023.

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