The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Resource Allocation of eMBB and URLLC Traffic using Pre-emption Mechanism
Abstract
5G and beyond networks accommodate eMBB service for high throughput needs while URLLC service provides stringent latency and reliability requirements with relatively small packet sizes. In this paper, we study resource allocation for jointly considering the requirements of eMBB and URLLC services, where codeblock group-based transmission and preemption mechanisms in 5G NR standards are utilized. The proposed algorithm, which is called Patient and Intelligent Pre-Empter (PIPE), waits for a suitable symbol time to pre-empt an eMBB traffic for scheduling URLLC traffic. The selection of punctured eMBB packets is done intelligently to minimize the eMBB throughput degradation using puncture allowance rates from error coding schemes. Simulation results show that the eMBB throughput is maximized by sufficiently delaying the scheduling of URLLC traffic without violating its delay constraint.
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The priority assumption can be supported by mechanisms developed for 5G, such as network slicing or preemption techniques.
“This priority assumption can be supported by mechanisms developed for 5G, such as network slicing or preemption techniques.”
From Cross-layer latency analysis for 5G NR in V2X communications (2025) · cites this paper as [26]
Citations
Cites
5G NR: the Next Generation Wireless Access Technology
Dahlman, E., Parkvall, S., & Sköld, J. (2018) · Elsevier eBooks · doi:10.1016/c2017-0-01347-2 · Cited by 877
5G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technology
Lin, X., Grövlen, A., Werner, K., Li, J., Baldemair, R., Cheng, J.-F. T., Parkvall, S., Larsson, D. C., Koorapaty, H., Frenne, M., et al. (2019) · IEEE Communications Standards Magazine · doi:10.1109/mcomstd.001.1800036 · Cited by 307
A Deep-Reinforcement-Learning-Based Approach to Dynamic eMBB/URLLC Multiplexing in 5G NR
Huang, Y., Li, S., Li, C., Hou, Y. T., & Lou, W. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2978692 · Cited by 91
Reinforcement Learning-Based Radio Resource Control in 5G Vehicular Network
Zhou, Y., Tang, F., Kawamoto, Y., & Kato, N. (2019) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2019.2962409 · Cited by 61
Alsenwi, M., Pandey, S. R., Tun, Y. K., Kim, K. T., & Hong, C. S. (2019) · International Conference on Information Networking · doi:10.1109/icoin.2019.8718159 · Cited by 42
Stochastic Optimization of URLLC-eMBB Joint Scheduling With Queuing Mechanism
Zhang, W., Derakhshani, M., & Lambotharan, S. (2020) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2020.3046628 · Cited by 37
Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic
Saggese, F., Pasqualini, L., Moretti, M., & Abrardo, A. (2021) · Globecom · doi:10.1109/globecom46510.2021.9685777 · Cited by 29
A Deep-Learning-based Link Adaptation Design for eMBB/URLLC Multiplexing in 5G NR
Huang, Y., Hou, Y. T., & Lou, W. (2021) · doi:10.1109/infocom42981.2021.9488790 · Cited by 22
Resource Allocation of URLLC and eMBB Mixed Traffic in 5G Networks: A Deep Learning Approach
Abdelsadek, M. Y., Gadallah, Y., & Ahmed, M. H. (2020) · Globecom · doi:10.1109/globecom42002.2020.9322163 · Cited by 17
Performance Evaluation of a Live Multi-Site LTE Network
Sezgin, G., Coşkun, Y., Başar, E., & Kurt, G. K. (2018) · IEEE Access · doi:10.1109/access.2018.2868385 · Cited by 9
Timely Scheduling of URLLC Packets Using Precoder Compatibility Estimates
Ksairi, N., & Kountouris, M. (2019) · IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · doi:10.1109/pimrc.2019.8904870 · Cited by 6
Traffic Characteristics of Extended RealityPreprint
Alnajim, A. M., Salehi, S., Shen, C.-C., & Smith, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2304.07908 · Cited by 5
Flagged first, then by citations
Research works printed but not linked: 3
- 0, 3GPP TS 38 212 - NR Multiplexing and channel coding v17 4
- 0, 3GPP TS 38 214 NR Physical layer procedures for data
- xia, 2020, Verticals URLLC Use Cases and Requirements
Cited by
Sorted by citations
Cross-layer latency analysis for 5G NR in V2X communications
Horta, J., Siller, M., & Villarreal-Reyes, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0313772 · Cited by 18
Engin, E., Hökelek, İ., Görçin, A., & Çırpan, H. A. (2025) · IEEE Access · doi:10.1109/access.2025.3536997 · Cited by 13
Self-citationAboud, M. A., Zangar, N., Langar, R., Berbineau, M., & Madec, J. (2025) · Global Information Infrastructure and Networking Symposium · doi:10.1109/giis64151.2025.10921737 · Cited by 3
A Simulation on Resource Allocation Policies: URLLC Packets Loss and eMBB Packets Waiting Time
Altawil, R., Zahr, S. A., Audebert, V., Decreusefond, L., & Martins, P. (2025) · Global Information Infrastructure and Networking Symposium · doi:10.1109/giis64151.2025.10922079 · Cited by 1
Optimal Resource Allocation in 5G RAN with Co-Existing eMBB and uRLLC Applications
Sayeed, A., & Bera, S. (2026) · International Conference on Information Networking · doi:10.1109/icoin68469.2026.11480604
Engin, E., Hökelek, İ., & Çırpan, H. A. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2026.3706527
Self-citation
Metadata
- Authors
- Engin, E., Hökelek, I., & Çırpan, H. A.
- Affiliations
- Istanbul Technical University (TR)
- Istanbul Technical University,Electronics and Communication Engineering Department,Istanbul,Turkey
- Venue
- 2023 46th International Conference on Telecommunications and Signal Processing (TSP)
- Publisher
- IEEE
- Published
- 2023-07-12
- Pages
- 129-133
- OpenAlex
- W4385577214
- Cited by
- 6
- References
- 12
- Citations by year
- 2026: 2 · 2025: 4
- FWCI
- 2.94
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Telecommunications and Signal Processing (TSP) (publishedVersion)
- Istanbul Technical University Academic Open Archive (Istanbul Technical University) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Network Optimization
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Keywords
- resource allocation, enhanced mobile broadband, ultra-reliable low-latency communication, 5G NR, delay constraints
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