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A Traffic Feature Analysis Approach for Converged Networks of LTE and Broadband Carrier Wireless Communications

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Simulation Tools and Techniques (SIMUtools 2020)

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Abstract

With the emergence of new requirements for the application of network access network, network traffic presents new characteristics, and network management faces new challenges. The main contribution of this paper is to propose a new network traffic model and prediction method based on generalized linear regression model. Firstly, the network traffic is modeled and generalized linear regression model is used to model it. Then, using the generalized linear regression theory, we can calculate the modified parameters and determine the appropriate model, so that we can accurately predict the network traffic. The simulation results show that the method is feasible.

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Author information

Authors and Affiliations

  1. Electric Power Research Institute of State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang, 110006, China

    Huan Li, Yang Liu & Zhibin Yang

  2. State Grid Liaoning Electric Power Supply Co., Ltd., Shenyang, 110004, China

    Fanbo Meng & Dongdong Wang

  3. BaoDing HaoYuan Electric Technology Co., Ltd., Baoding, 071000, Hebei, China

    Yang Nan

Authors
  1. Huan Li
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  2. Yang Liu
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  3. Fanbo Meng
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  4. Zhibin Yang
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  5. Dongdong Wang
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  6. Yang Nan
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Editor information

Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, H., Liu, Y., Meng, F., Yang, Z., Wang, D., Nan, Y. (2021). A Traffic Feature Analysis Approach for Converged Networks of LTE and Broadband Carrier Wireless Communications. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_17

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  • DOI: https://doi.org/10.1007/978-3-030-72792-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72791-8

  • Online ISBN: 978-3-030-72792-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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