Contract-based scheduling of URLLC packets in incumbent Embb traffic

Aunas Manzoor, S. M. Ahsan Kazmi, Shashi Raj Pandey, Choong Seon Hong*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

39 Citations (Scopus)

Abstract

Recently, the coexistence of ultra-reliable and low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services on the same licensed spectrum has gained a lot of attention from both academia and industry. However, the coexistence of these services is not trivial due to the diverse multiple access protocols, contrasting frame distributions in the existing network, and the distinct quality of service requirements posed by these services. Therefore, such coexistence drives towards a challenging resource scheduling problem. To address this problem, in this paper, we first investigate the possibilities of scheduling URLLC packets in incumbent eMBB traffic. In this regard, we formulate an optimization problem for coexistence by dynamically adopting a superposition or puncturing scheme. In particular, the aim is to provide spectrum access to the URLLC users while reducing the intervention on incumbent eMBB users. Next, we apply the one-to-one matching game to find stable URLLC-eMBB pairs that can coexist on the same spectrum. Then, we apply the contract theory framework to design contracts for URLLC users to adopt the superposition scheme. Simulation results reveal that the proposed contract-based scheduling scheme achieves up to 63% of the eMBB rate for the ‘‘No URLLC’’ case compared to the ‘‘Puncturing’’ scheme.

Original languageEnglish
JournalIEEE Access
Volume8
Pages (from-to)167516-167526
Number of pages11
ISSN2169-3536
DOIs
Publication statusPublished - 2020
Externally publishedYes

Bibliographical note

Funding Information:
This work was partially supported by the Institute of Information communications Technology Planning Evaluation (IITP) grant funded by the Korean Government Ministry of Science and ICT (MSIT) (No. 2019-0-01287, Evolvable Deep Learning Model Generation Platform for Edge Computing).

Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Keywords

  • Contract theory
  • Enhance mobile broadband (eMBB)
  • Fifth-generation new radio
  • Low-latency communication (URLLC)
  • Matching theory
  • Scheduling
  • Ultra-reliable

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