Welcome to Journal of Beijing Institute of Technology
Advance Search
Volume 31Issue 5
Oct. 2022
Turn off MathJax
Article Contents
Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2022> 31(5): 473-482
Shiqi Zhang, Guoxin Yu, Shanping Yu, Yanjun Zhang, Yu Zhang. LSTM-Based Adaptive Modulation and Coding for Satellite-to-Ground Communications[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(5): 473-482. doi: 10.15918/j.jbit1004-0579.2021.101
Citation: Shiqi Zhang, Guoxin Yu, Shanping Yu, Yanjun Zhang, Yu Zhang. LSTM-Based Adaptive Modulation and Coding for Satellite-to-Ground Communications[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2022, 31(5): 473-482.doi:10.15918/j.jbit1004-0579.2021.101
shu

LSTM-Based Adaptive Modulation and Coding for Satellite-to-Ground Communications

doi:10.15918/j.jbit1004-0579.2021.101
  • 1.

    School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

  • 2.

    School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China

Funds:This work was supported by the National High Technology Research and Development Program of China (No. 2020YFB1806004).
More Information
  • Author Bio:

    Shiqi Zhangreceived the B.S. degree from the School of Information and Electronics, Beijing Institute of Technology, Beijing, China in 2018. He is currently a Postgraduate student in Communication and Network Laboratory, Beijing Institute of Technology. His research interests include communication, low earth orbit, and artificial intelligence

    Guoxin Yureceived the B.S. degree from the Schcool of Information and Electronics, Beijing Institute of Technology, Beijing, China in 2021. He is currently reading a master’s degree with the Aerospace Network Information Technology Research Institute, Beijing Institute of Technology, Beijing, China. His research interests include artificial intelligence, deep learning and earth integrated network

    Shanping Yureceived her B.S. degree in applied mathematics from Central South University, Changsha, China in 2012. She received her Ph.D. degree in the Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. She is a postdoc in the School of Information and Electronics, Beijing Institute of Technology. Her research interests include social computing, artificial intelligence, and quantum information

    Yanjun Zhangreceived the B.S., Master, and Ph.D. degrees from Tsinghua University. He is currently an associate professor at the School of Cyberspace Science and Technology, Beijing Institute of Technology, China. His currently researches mainly focus on low-power & high-performance integrated circuits design, FPGA based high-performance computation method, and low-power asynchronous circuit designing methods

    Yu Zhangreceived the B.S. degree in electrical engineering from University of Electronic Science and Technology of China in 1995, the M.S. degree in electrical engineering from Beijing Institute of Technology in 1997, and the Ph.D. degree in electrical engineering from Peking University in 2001. He is currently an Assistant Professor with the School of Information and Electronics, Beijing Institute of Technology. His research interests include communications, internet of things, and space-based networks

  • Corresponding author:ysp@bit.edu.cn
  • Received Date:2021-12-14
  • Rev Recd Date:2022-03-29
  • Accepted Date:2022-04-02
  • Publish Date:2022-10-31
    Abstract
  • Satellite communication develops rapidly due to its global coverage and is unrestricted to the ground environment. However, compared with the traditional ground TCP/IP network, a satellite-to-ground link has a more extensive round trip time (RTT) and a higher packet loss rate, which takes more time in error recovery and wastes precious channel resources. Forward error correction (FEC) is a coding method that can alleviate bit error and packet loss, but how to achieve high throughput in the dynamic network environment is still a significant challenge. Inspired by the deep learning technique, this paper proposes a signal-to-noise ratio (SNR) based adaptive coding modulation method. This method can maximize channel utilization while ensuring communication quality and is suitable for satellite-to-ground communication scenarios where the channel state changes rapidly. We predict the SNR using the long short-term memory (LSTM) network that considers the past channel status and real-time global weather. Finally, we use the optimal matching rate (OMR) to evaluate the pros and cons of each method quantitatively. Extensive simulation results demonstrate that our proposed LSTM-based method outperforms the state-of-the-art prediction algorithms significantly in mean absolute error (MAE). Moreover, it leads to the least spectrum waste.
    • FullText(HTML)
  • loading
    • References (22)
  • [1]
    M. Alblet, “Attachment A to the FCC’s license approval for spacex nongeostationary satellite system,” Federal Communications Commission (FCC), Washington D. C., USA, April 26, 2019.
    [2]
    S. Cheng, H. Hu, X. Zhang, and Z. Guo, “DeepRS: Deep-learning based network-adaptive FEC for real-time video communications,” in 2020 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-5, 2020.
    [3]
    X. Wang, H. Li, and Q. Wu, “Optimizing adaptive coding and modulation for satellite network with ML-based CSI prediction,” in 2019 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6, 2019.
    [4]
    Y. Xie, J. C. Mu, and J. Yuan, “Design of rate-compatible protograph-based LDPC codes with mixed circulants,” in 2010 6th International Symposium on Turbo Codes & Iterative Information Processing, pp. 434-438, 2010.
    [5]
    K. J. Hole, H. Holm, and G. E. Oien,“Adaptive multidimensional coded modulation over flat fading channels,” IEEE Journal on Selected Areas in Communications, vol. 18, no. 7, pp. 1153-1158, 2000. doi:10.1109/49.857915
    [6]
    R. Daniels and R. W. Heath, “Online adaptive modulation and coding with support vector machines,” in 2010 European Wireless Conference (EW), pp. 718–724, 2010.
    [7]
    A. Tsakmalis, S. Chatzinotas, and B. Ottersten, “Automatic modulation classification for adaptive power control in cognitive satellite communications,” in 2014 7th Advanced Satellite Multimedia Systems Conference and the 13th Signal Processing for Space Communications Workshop (ASMS/SPSC), pp. 234–240, 2014.
    [8]
    M. Awad and R. Khanna, Support vector regression in efficient learning machines. Berkeley, CA: Apress Press, 2015.
    [9]
    C. Cortes and V. Vapnik,“Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
    [10]
    S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735-1780, 1997.
    [11]
    M. AbdelMoniem, S. M. Gasser, M. S. ElMahallawy, M. W. Fakhr, and A. Soliman,“Enhanced noma system using adaptive coding and modulation based on LSTM neural network channel estimation,” Applied Sciences, vol. 9, no. 15, pp. 3022, 2019. doi:10.3390/app9153022
    [12]
    A. Morello and V. Mignone,“Dvb-s2: The second generation standard for satellite broad-band services,” Proceedings of the IEEE, vol. 94, no. 1, pp. 210-227, 2006. doi:10.1109/JPROC.2005.861013
    [13]
    R. Howell, “Earth-space propagation: Recommendation ITU-R P. 618,” IET Digital Library, 2003.
    [14]
    E. Alberty, S. Defever, C. Moreau, R. De Gaudenzi, A. Ginesi, R. Rinaldo, G. Gallinaro, and A. Vernucci,“Adaptive coding and modulation for the dvb-s2 standard interactive applications: capacity assessment and key system issues,” IEEE Wireless Communications, vol. 14, no. 4, pp. 61-69, 2007. doi:10.1109/MWC.2007.4300985
    [15]
    D. Divsalar, S. Dolinar, and C. Jones, “Low-rate ldpc codes with simple protograph structure,” in Proceedings of International Symposium on Information Theory 2005, pp. 1622-1626, 2005.
    [16]
    D. Divsalar, S. Dolinar, and C. Jones, “Construction of protograph LDPC codes with linear minimum distance,” in 2006 IEEE International Symposium on Information Theory, pp. 664-668, 2006.
    [17]
    D. J. MacKay,“Good error-correcting codes based on very sparse matrices,” IEEE Transactions on Information Theory, vol. 45, no. 2, pp. 399-431, 1999. doi:10.1109/18.748992
    [18]
    D. J. MacKay and R. M. Neal,“Near Shannon limit performance of low density parity check codes,” Electronics Letters, vol. 32, no. 18, pp. 1645-1646, 1996. doi:10.1049/el:19961141
    [19]
    CCSDS, Low density parity check codes for use in near-earth and deep space applications. Exp. Specification CCSDS 131.1-O-2. Washington D C, USA: CCSDS Press, 2007.
    [20]
    T. Van Nguyen, A. Nosratinia, and D. Divsalar, “Threshold of protograph-based LDPC coded BICM for Rayleigh fading,” in 2011 IEEE Global Telecommunications Conference-GLOBECOM 2011, pp. 1-5, 2011.
    [21]
    J. Zhang, K. Tan, J. Zhao, H. Wu, and Y. Zhang, “A practical SNR-guided rate adaptation,” in IEEE INFOCOM 2008-the 27th Conference on Computer Communications, pp. 2083-2091, 2008.
    [22]
    G.-H. Tzeng and J.-J. Huang, Multiple attribute decision making: Methods and applications. New York, USA: CRC Press, 2011.
    • Relative Articles
  • Supplements (0)
  • Cited By
  • 加载中

Catalog

    通讯作者:陈斌, bchen63@163.com
    • 1.

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(6)/Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (174) PDF downloads(26) Cited by()
    Proportional views
    Related

    Address:5 Zhongguancun South Street, Haidian District, Beijing

    Tel:86-10-68914374

    Email:blgywb@bit.edu.cn

    Copy Right © Journal of Beijing Institute of Technology

    Supported by:Beijing Renhe Information Technology Co. Ltd

    /

    Return
    Return
      Baidu
      map