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Engineering Group Mini Review Article ID: igmin230

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

Optical Engineering
Zhi Liu 1 ,
Qingfang Jiang * 2 ,
Kanglian Zhao 2 ,
Xianzhu Liu 1 ,
Wanzhuo Ma 1 and
Xiaolong Ni 1
Affiliation

Affiliation

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

    Nanjing University, Nanjing, Jiangsu, China

    Nanjing University, Nanjing, Jiangsu, China

    Nanjing University, Nanjing, Jiangsu, China

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

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

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

DOI10.61927/igmin230 Fulltext HTML Fulltext PDF Cite this article
DOI10.61927/igmin230 Fulltext HTML Fulltext PDF Cite this article

Abstract

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.

Figures

Schematic Diagram of Satellite-to-Ground Laser Communication Link. Figure 1: Schematic Diagram of Satellite-to-Ground Laser Com...
Schematic Diagram of Approaches for Optimizing the Transmission Performance of Satellite-to-Ground Laser Communication Links. Figure 2: Schematic Diagram of Approaches for Optimizing the...
Optical Downlink Satellite Laser Communication with Multiple Ground Stations. Figure 3: Optical Downlink Satellite Laser Communication wit...
Illustrates a schematic diagram of cloud prediction for high-orbit satellite communication application scenarios. Figure 4: Illustrates a schematic diagram of cloud predictio...
Illustrates a schematic diagram of link transmission quality prediction in a low Earth orbit satellite-ground laser communication scenario. Figure 5: Illustrates a schematic diagram of link transmissi...

References

    1. Ding R, Fang T, Wang W, et al. Star-Ground Fusion Communication System: Network Architecture, Enabling Technologies, and Prototype Verification. Space Integr Ground Inf Networks. 2023;4(03):79-87.
    2. Jiang H, An Y, Zhang Y, et al. Current Status, Development Trends, and Key Technologies Analysis of Space Laser Communication. J Aerosp Inf Syst. 2015;34(03):207-217.
    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.
    5. Hu M, Zhou Y, Zhao K, et al. Cloud Prediction Technology for Reliable Networking of Future Star-Ground Laser Backbone Links. Mobile Commun. 2023;47(10):58-64.
    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.
    9. 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.
    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.
    14. Jiang Q, Liu Z, Yao H, et al. Performance of Adaptive Bit-Interleaved Polar Coded Modulation in FSOC System. Photonics. MDPI. 2023;11(1):34.
    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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