Conference proceeding
Contention-Free Configured Grant Scheduling for 5G URLLC Traffic
2023 60th ACM/IEEE Design Automation Conference (DAC), Vol.2023-, pp.1-6
07/09/2023
DOI: 10.1109/DAC56929.2023.10247842
Abstract
5G networks are being designed to support ultra reliable and low latency communication (URLLC) services in many real-time industrial applications. The conventional grant-based dynamic scheduling can hardly fulfill the URLLC requirements due to the non-negligible transmission delays introduced during the spectrum resource grant process. To address this problem, 5G defines a grant-free transmission scheme, namely configured grant (CG) scheduling, for uplink (UL) traffic to pre-allocate spectrum resource to user equipments (UEs). This paper studies CG scheduling for periodic URLLC traffic with real-time and collision-free guarantees. An exact solution based on Satisfiability Modulo Theory (SMT) is first proposed to generate a feasible CG configuration for a given traffic set. To enhance scalability, we further develop an efficient graph-based heuristic consisting of an offset selection method and a multicoloring algorithm for spectrum resource allocation. Extensive experiments are conducted using 3GPP industrial use cases to show that both approaches can satisfy the real-time and collision-free requirements, and the heuristic can achieve comparable schedulability ratio with the SMT-based approach but require significantly lower running time.
Details
- Title: Subtitle
- Contention-Free Configured Grant Scheduling for 5G URLLC Traffic
- Creators
- Tianyu Zhang - University of ConnecticutXiaobo Sharon Hu - University of Notre DameSong Han - University of Connecticut
- Resource Type
- Conference proceeding
- Publication Details
- 2023 60th ACM/IEEE Design Automation Conference (DAC), Vol.2023-, pp.1-6
- Publisher
- IEEE
- DOI
- 10.1109/DAC56929.2023.10247842
- ISSN
- 0738-100X
- Grant note
- National Science Foundation (10.13039/100000001)
- Language
- English
- Date published
- 07/09/2023
- Academic Unit
- Computer Science
- Record Identifier
- 9984696711002771
Metrics
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