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Stochastic optimization of URLLC-eMBB joint scheduling with queuing mechanism

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journal contribution
posted on 07.01.2021, 11:07 by Wenheng Zhang, Mahsa DerakhshaniMahsa Derakhshani, Sangarapillai LambotharanSangarapillai Lambotharan
This paper proposes a dynamic joint scheduling for the ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) traffic at a sub-frame level with a queuing mechanism, which monitors and controls the latency of each URLLC packet in real time to ensure its strict requirements. We analytically derive the outage probability (i.e., the probability of any URLLC packet drop over all transmission channels) and URLLC expected throughput in addition to the expected value of served URLLC packets. Then, a stochastic optimization problem is formulated to maximize the total throughput for the eMBB services, constraining the URLLC outage probability. Numerical results confirm effectiveness of our queuing policy to significantly reduce the URLLC loss rate while ensuring that the total eMBB throughput is not affected.

Funding

Royal Academy of Engineering under the Leverhulme Trust Research Fellowship scheme (DerakhshaniLTRF1920\16\67)

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IEEE Wireless Communications Letters

Volume

10

Issue

4

Pages

844-848

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

14/12/2020

Publication date

2020-12-22

Copyright date

2020

ISSN

2162-2337

eISSN

2162-2345

Language

en

Depositor

Dr Mahsa Derakhshani. Deposit date: 4 January 2021

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