IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0224883.html
   My bibliography  Save this article

Adaptive fuzzy flow rate control considering multifractal traffic modeling and 5G communications

Author

Listed:
  • Alisson Assis Cardoso
  • Flávio Henrique Teles Vieira

Abstract

In this paper, we propose a predictive Generalized OBF (Orthonormal Basis Functions)-Fuzzy flow control scheme for the 5G downlink by deriving an expression for the optimal control rate of the traffic sources considering minimization of data delay and a minimum traffic rate to the users. The adaptive GOBF-Fuzzy model is applied to predict queueing behavior in initial 5G systems. To this end, we propose to obtain orthonormal basis functions related to the real traffic flows via multifractal modeling, inserting these functions into the fuzzy model trained with the LMS (Least Mean Square) adaptive algorithm. Simulations of a F-OFDM (Filtered Orthogonal Frequency Division Multiplexing) based 5G Downlink are carried out to validate the proposed flow control algorithm. Comparisons with other predictive control schemes in the literature prove the efficiency of the adaptive GOBF-fuzzy based control in enhancing the performance of the system downlink as well as guaranteeing some QoS (Quality of Service) parameters.

Suggested Citation

  • Alisson Assis Cardoso & Flávio Henrique Teles Vieira, 2019. "Adaptive fuzzy flow rate control considering multifractal traffic modeling and 5G communications," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-22, November.
    • Handle: RePEc:plo:pone00:0224883
    DOI: 10.1371/journal.pone.0224883
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0224883
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0224883&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0224883?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Tang, Jinjun & Chen, Xinqiang & Hu, Zheng & Zong, Fang & Han, Chunyang & Li, Leixiao, 2019. "Traffic flow prediction based on combination of support vector machine and data denoising schemes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Junjie Fu & Xinqiang Chen & Shubo Wu & Chaojian Shi & Huafeng Wu & Jiansen Zhao & Pengwen Xiong, 2020. "Mining ship deficiency correlations from historical port state control (PSC) inspection data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    2. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    3. Yan, Ying & Zhang, Ying & Yang, Xiangli & Hu, Jin & Tang, Jinjun & Guo, Zhongyin, 2020. "Crash prediction based on random effect negative binomial model considering data heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    4. Shenghan Zhou & Chaofan Wei & Chaofei Song & Yu Fu & Rui Luo & Wenbing Chang & Linchao Yang, 2022. "A Hybrid Deep Learning Model for Short-Term Traffic Flow Pre-Diction Considering Spatiotemporal Features," Sustainability, MDPI, vol. 14(16), pages 1-14, August.
    5. Peng, Yanni & Xiang, Wanli, 2020. "Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    6. Du, Zhouyang & Tang, Jinjun & Qi, Yong & Wang, Yiwei & Han, Chunyang & Yang, Yifan, 2020. "Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city—China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    7. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    8. Liu, Yang & Song, Yaolun & Zhang, Yan & Liao, Zhifang, 2022. "WT-2DCNN: A convolutional neural network traffic flow prediction model based on wavelet reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    9. Wu, Jiaxin & Zhou, Xubing & Peng, Yi & Zhao, Xiaojun, 2022. "Recurrence analysis of urban traffic congestion index on multi-scale," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    10. Xinqiang Chen & Jinquan Lu & Jiansen Zhao & Zhijian Qu & Yongsheng Yang & Jiangfeng Xian, 2020. "Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    11. Fang Zong & Meng Zeng & Yang Cao & Yixuan Liu, 2021. "Local Dynamic Path Planning for an Ambulance Based on Driving Risk and Attraction Field," Sustainability, MDPI, vol. 13(6), pages 1-13, March.
    12. Rasaizadi, Arash & Farzin, Iman & Hafizi, Fateme, 2022. "Machine learning approach versus probabilistic approach to model the departure time of non-mandatory trips," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    13. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    14. Tang, Jinjun & Zhang, Xinshao & Yu, Tianjian & Liu, Fang, 2021. "Missing traffic data imputation considering approximate intervals: A hybrid structure integrating adaptive network-based inference and fuzzy rough set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    15. Lunacek, Monte & Williams, Lindy & Severino, Joseph & Ficenec, Karen & Ugirumurera, Juliette & Eash, Matthew & Ge, Yanbo & Phillips, Caleb, 2021. "A data-driven operational model for traffic at the Dallas Fort Worth International Airport," Journal of Air Transport Management, Elsevier, vol. 94(C).
    16. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    17. Tang, Jinjun & Hu, Jin & Hao, Wei & Chen, Xinqiang & Qi, Yong, 2020. "Markov Chains based route travel time estimation considering link spatio-temporal correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    18. Unsok Ryu & Jian Wang & Unjin Pak & Sonil Kwak & Kwangchol Ri & Junhyok Jang & Kyongjin Sok, 2022. "A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis," Transportation, Springer, vol. 49(3), pages 951-988, June.
    19. Huang, Haichao & Chen, Jingya & Sun, Rui & Wang, Shuang, 2022. "Short-term traffic prediction based on time series decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0224883. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.