Real-Time Flow Scheduling in Industrial 5G New Radio

Tianyu Zhang; Jiachen Wang; Xiaobo Sharon Hu; Song Han

doi:10.1109/RTSS59052.2023.00039

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Real-Time Flow Scheduling in Industrial 5G New Radio

Tianyu Zhang, Jiachen Wang, Xiaobo Sharon Hu and Song Han

2023 IEEE Real-Time Systems Symposium (RTSS), pp.371-384

12/05/2023

DOI: 10.1109/RTSS59052.2023.00039

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Abstract

Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling industrial data flows to meet their hard real-time requirements. In this paper, we first leverage a real-world 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantees through time-frequency-space resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct the schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. To evaluate the performance of 5G-TPS, we present a case study of a motion control panel use case and perform extensive experiments. The results show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers.

Dynamic scheduling

Job shop scheduling

New Radio

Real-time systems

Resource management

Schedules

Throughput

Details

Title: Subtitle: Real-Time Flow Scheduling in Industrial 5G New Radio
Creators: Tianyu Zhang - University of Connecticut
Jiachen Wang - University of Connecticut
Xiaobo Sharon Hu - University of Notre Dame
Song Han - University of Connecticut
Resource Type: Conference proceeding
Publication Details: 2023 IEEE Real-Time Systems Symposium (RTSS), pp.371-384
Publisher: IEEE
DOI: 10.1109/RTSS59052.2023.00039
ISSN: 1052-8725
eISSN: 2576-3172
Grant note: CNS-1932480,CNS-2008463,CCF-2028875 / National Science Foundation (10.13039/100000001)
Language: English
Date published: 12/05/2023
Academic Unit: Computer Science
Record Identifier: 9984696709902771

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