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Deep Learning-Based Network Channel Quality Prediction Algorithm in High-Speed Mobile Scenarios

Published: 28 February 2024 Publication History

Abstract

As a comfortable and fast new generation popular transportation, high-speed rail(HSR) has benefited from the development of mobile Internet technology, and passengers' demand for information during HSR journeys has been increasing. At the same time, the demand for streaming media services by users is also constantly increasing. However, in high-speed mobile scenarios, streaming media transmission faces frequent network channel quality fluctuations and unpredictable issues. To address these problems, this paper proposes a channel quality prediction algorithm based on Long Short Term Memory Network (LSTM) and Convolutional Neural Network (CNN), called the CNN-LSTM model. This model can predict the available channel quality in real-time and improve the efficiency of streaming media transmission, enhancing the user's viewing experience. Through extensive simulation experiments, the feasibility and effectiveness of the channel quality prediction algorithm are validated.

References

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China State Railway Group Limited. https://www.mot.gov.cn/fenxigongbao/hangyegongbao/202305/P020230530535262349964.pdf
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Tian-Yue Zheng,Tai-Yang Ling,Zhi-Wei Yao A massive MIMO channel prediction method based on improved Kalman filtering[J]. Radio Communication Technology,2021,47(04):459-465.
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SUN Dechun,LI Yu. An improved channel prediction algorithm for HOSVD noise reduction[J]. Journal of Harbin Institute of Technology,2020,52(04):47-51.
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C. Liu, X. Liu, D. W. Kwan Ng and J. Yuan, "Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems," ICC 2021 - IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-7.
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X. Liu, C. Liu, Y. Li, B. Vucetic and D. W. K. Ng, "Deep Residual Learning-Assisted Channel Estimation in Ambient Backscatter Communications," in IEEE Wireless Communications Letters, vol. 10, no. 2, pp. 339-343, Feb. 2021.
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R. Sattiraju, A. Weinand and H. D. Schotten, "Channel Estimation in C-V2X using Deep Learning," 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Goa, India, 2019, pp. 1-5.
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Shuang Xu, Yongxin Wen, Wenbin Liu Fusion of multilayer attention mechanism and CNN-LSTM for backscatter channel prediction[J/OL]. Small Microcomputer Systems:1-8[2023-07-26].http://kns.cnki.net/kcms/detail/21.1106.TP.20230711.1337.010.html.
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  1. Deep Learning-Based Network Channel Quality Prediction Algorithm in High-Speed Mobile Scenarios

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 28 February 2024

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    Author Tags

    1. High-speed rail
    2. channel quality prediction
    3. deep learning
    4. network

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    • Natural Science Foundation of China

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    ICCPR 2023

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