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

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  • 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
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    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).
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