Bayesian optimization of queuing-based multi-channel URLLC scheduling

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 flex...

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Main Authors:	Wenheng Zhang, Mahsa Derakhshani, Gan Zheng, Chung Shue Chen, Sangarapillai Lambotharan
Format:	Default Article
Published:	2022
Subjects:	Distributed computing and systems software not elsewhere classified Bayesian optimization dynamic scheduling eMBB punctured scheduling queuing URLLC Distributed Computing
Online Access:	https://hdl.handle.net/2134/21181084.v1
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id	rr-article-21181084
record_format	Figshare
spelling	rr-article-211810842022-09-21T00:00:00Z Bayesian optimization of queuing-based multi-channel URLLC scheduling Wenheng Zhang (11679769) Mahsa Derakhshani (2572993) Gan Zheng (7122788) Chung Shue Chen (7212866) Sangarapillai Lambotharan (1252278) Distributed computing and systems software not elsewhere classified Bayesian optimization dynamic scheduling eMBB punctured scheduling queuing URLLC Distributed Computing <p>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.</p> 2022-09-21T00:00:00Z Text Journal contribution 2134/21181084.v1 https://figshare.com/articles/journal_contribution/Bayesian_optimization_of_queuing-based_multi-channel_URLLC_scheduling/21181084 CC BY 4.0
institution	Loughborough University
collection	Figshare
topic	Distributed computing and systems software not elsewhere classified Bayesian optimization dynamic scheduling eMBB punctured scheduling queuing URLLC Distributed Computing
spellingShingle	Distributed computing and systems software not elsewhere classified Bayesian optimization dynamic scheduling eMBB punctured scheduling queuing URLLC Distributed Computing Wenheng Zhang Mahsa Derakhshani Gan Zheng Chung Shue Chen Sangarapillai Lambotharan Bayesian optimization of queuing-based multi-channel URLLC scheduling
description	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.
format	Default Article
author	Wenheng Zhang Mahsa Derakhshani Gan Zheng Chung Shue Chen Sangarapillai Lambotharan
author_facet	Wenheng Zhang Mahsa Derakhshani Gan Zheng Chung Shue Chen Sangarapillai Lambotharan
author_sort	Wenheng Zhang (11679769)
title	Bayesian optimization of queuing-based multi-channel URLLC scheduling
title_short	Bayesian optimization of queuing-based multi-channel URLLC scheduling
title_full	Bayesian optimization of queuing-based multi-channel URLLC scheduling
title_fullStr	Bayesian optimization of queuing-based multi-channel URLLC scheduling
title_full_unstemmed	Bayesian optimization of queuing-based multi-channel URLLC scheduling
title_sort	bayesian optimization of queuing-based multi-channel urllc scheduling
publishDate	2022
url	https://hdl.handle.net/2134/21181084.v1
_version_	1796732390201098240

Bayesian optimization of queuing-based multi-channel URLLC scheduling

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