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Model for Analyzing Impact of Path Loss on eMBB Bit Rate Degradation Under Priority URLLC Transmission in 5G Network

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Distributed Computer and Communication Networks: Control, Computation, Communications (DCCN 2022)

Abstract

One of the challenging tasks in 5G networks is to organize a joint URLLC (ultra-reliable and low-latency communication) and eMBB (enhanced mobile broadband) transmission in such a way that provides the priority to URLLC connections. The eMBB users suffer quality of service degradation, primarily bit rate degradation, as well as service interruption. In the paper, we provide a queuing model for analyzing this effect depending on several path loss models. The queuing system is of type resource queuing system, where the resource has three-dimensional structure – frequency bandwidth, radio frame length, and transmitted signal power. Due to different URLLC and eMBB bit rate requirements, we use weighted round robin (WRR) resource allocation scheme. The stationary probability distribution depends on the conditional probabilities of session acceptance. We provide the formulas for calculating eMBB metrics – average bit rate, interruption probability, and blocking probability. A numerical example illustrates the impact of two path loss models for macro- and microcells on eMBB metrics.

The research was supported by the Ministry of Science and Higher Education of the Russian Federation, project No. 075-15-2020-799. The research was carried out using the infrastructure of the Shared Research Facilities “High Performance Computing and Big Data” (CKP “Informatics”) of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences.

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Correspondence to Irina Kochetkova .

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Kochetkova, I., Makeeva, E., Ageeva, A., Gorshenin, A. (2022). Model for Analyzing Impact of Path Loss on eMBB Bit Rate Degradation Under Priority URLLC Transmission in 5G Network. In: Vishnevskiy, V.M., Samouylov, K.E., Kozyrev, D.V. (eds) Distributed Computer and Communication Networks: Control, Computation, Communications. DCCN 2022. Lecture Notes in Computer Science, vol 13766 . Springer, Cham. https://doi.org/10.1007/978-3-031-23207-7_14

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  • DOI: https://doi.org/10.1007/978-3-031-23207-7_14

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