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Efficient Algorithm for Evaluating the Required Volume of Resource in Wireless Communication Systems under Joint Servicing of Heterogeneous Traffic for the Internet of Things

  • Stochastic Systems
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Abstract

We construct and study a mathematical model of the distribution of the resource for transmitting information for an isolated cell of an LTE standard mobile network with the joint servicing of heterogeneous traffic from Internet of Things devices. The model considers an arbitrary number of streams of multimedia traffic, which differ in the intensity of the arrival of communication sessions, the size of the resource used to service one session, the time of resource occupation, and the probability of a session being allowed to transmit the information stream. We determine quality of service characteristics for incoming sessions and construct an effective algorithm for estimating the amount of resource required to service given traffic flows with required quality. Efficiency of the algorithm is achieved as a result of the implementation of recursion with respect to the available resource and the use of normalized probabilities of model states during calculations. The algorithm is computationally stable and allows to solve the resource estimation problem many times faster than traditional approaches based on calculating the probabilities of all states for each resource value and their subsequent normalization. We give numerical examples illustrating the implementation features of developed computational procedures.

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Acknowledgments

This work was partially supported by the Russian Foundation for Basic Research, project no. 16-29-09497ofi-m.

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Correspondence to S. N. Stepanov or M. S. Stepanov.

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Russian Text © The Author(s), 2019, published in Avtomatika i Telemekhanika, 2019, No. 11, pp. 108–126.

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Stepanov, S.N., Stepanov, M.S. Efficient Algorithm for Evaluating the Required Volume of Resource in Wireless Communication Systems under Joint Servicing of Heterogeneous Traffic for the Internet of Things. Autom Remote Control 80, 2017–2032 (2019). https://doi.org/10.1134/S0005117919110067

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  • DOI: https://doi.org/10.1134/S0005117919110067

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