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Bayesian optimization of queuing-based multi-channel URLLC scheduling
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Zhang, Wenheng, Derakhshani, Mahsa, Zheng, Gan, Chen, Chung Shue and Lambotharan, Sangarapillai (2023) Bayesian optimization of queuing-based multi-channel URLLC scheduling. IEEE Transactions on Wireless Communications, 22 (3). pp. 1763-1778. doi:10.1109/TWC.2022.3206421 ISSN 1536-1276.
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WRAP-Bayesian-optimization-queuing-multi-channel-transaction-22.pdf - Accepted Version - Requires a PDF viewer. Download (952Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TWC.2022.3206421
Abstract
This paper studies the allocation of shared resources between ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) in the emerging 5G and beyond cellular networks. In this paper, we design a unique queuing mechanism for the joint eMBB/URLLC system. The aim is to flexibly schedule URLLC traffic to enhance the total eMBB throughput and the reliability of URLLC packets (i.e., the probability of not dropping URLLC packets in each mini-slot) while maintaining a satisfactory transmission latency as per the 3GPP requirements. Precisely, by deriving the steady-state probabilities of URLLC queue backlog analytically, we formulate a stochastic optimization problem to maximize the total normalized eMBB throughput and the URLLC utility. Due to the stochastic nature of the objective function, it is expensive to evaluate it for any set of inputs, and thus the Bayesian optimization is applied to obtain the optimal results of such a black-box objective function. Numerical results demonstrate that the proposed queuing mechanism never violates the latency requirement of the URLLC services but improves the reliability. It also enhances the total normalized eMBB throughput as compared to the method without queuing.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > QA Mathematics T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, 5G mobile communication systems, Wireless communication systems , Mathematical optimization, Dynamic programming , Machine-to-machine communications | |||||||||||||||
Journal or Publication Title: | IEEE Transactions on Wireless Communications | |||||||||||||||
Publisher: | IEEE | |||||||||||||||
ISSN: | 1536-1276 | |||||||||||||||
Official Date: | March 2023 | |||||||||||||||
Dates: |
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Volume: | 22 | |||||||||||||||
Number: | 3 | |||||||||||||||
Number of Pages: | 16 | |||||||||||||||
Page Range: | pp. 1763-1778 | |||||||||||||||
DOI: | 10.1109/TWC.2022.3206421 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2022 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. | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 2 November 2022 | |||||||||||||||
Date of first compliant Open Access: | 2 November 2022 | |||||||||||||||
RIOXX Funder/Project Grant: |
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