A Discrete-Time Model of the 5G New Radio Uplink Channel

The introduction of 5G comes with significant advancements over LTE in both core and radio domains, with a shift to microservices in the core and enhanced flexibility in the radio access network. While these changes boost performance, it comes at the cost of increased complexity in the radio access and core configuration, particularly in the radio domain. This complexity is heightened by use case-specific 5G deployments in campus and industrial networks, making the selection of optimal configuration parameters a challenging task. To address this, we present a discrete-time queueing model for the 5G New Radio uplink channel, capable of predicting key performance indicators (KPIs) such as the one-way delay as well as analyzing the channel’s impact on traffic streams. The model is validated through simulations and comparisons with 5G campus network measurements.

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Publication Details

Published
2025-06-02
DOI
https://doi.org/10.23919/itc-3665175.2025.11078612
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00

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A Discrete-Time Model of the 5G New Radio Uplink Channel

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A Discrete-Time Model of the 5G New Radio Uplink Channel

Thomas Zinner, Wolfgang Kellerer, Tobias Hoßfeld, Stefan Geißler, Stanislav Lange, Simon Raffeck, Laura Becker, Sebastian G. Grøsvik
article en

Abstract

The introduction of 5G comes with significant advancements over LTE in both core and radio domains, with a shift to microservices in the core and enhanced flexibility in the radio access network. While these changes boost performance, it comes at the cost of increased complexity in the radio access and core configuration, particularly in the radio domain. This complexity is heightened by use case-specific 5G deployments in campus and industrial networks, making the selection of optimal configuration parameters a challenging task. To address this, we present a discrete-time queueing model for the 5G New Radio uplink channel, capable of predicting key performance indicators (KPIs) such as the one-way delay as well as analyzing the channel’s impact on traffic streams. The model is validated through simulations and comparisons with 5G campus network measurements.

Norwegian University of Science and Technology (NO), Technical University of Munich (DE)
HORIZON EUROPE Framework Programme
Openalex Percentile: Top 82%
Advanced MIMO Systems Optimization
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