Multi-Agent Multi-Armed Bandit Learning for Grant-Free Access in Ultra-Dense IoT Networks

Raza, MA; Abolhasan, M; Lipman, J; Shariati, N; Ni, W; Jamalipour, A

Multi-Agent Multi-Armed Bandit Learning for Grant-Free Access in Ultra-Dense IoT Networks

Raza, MA Abolhasan, M

Lipman, J

Shariati, N Ni, W

Jamalipour, A

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Cognitive Communications and Networking, 2024, PP, (99), pp. 1-1
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Raza, MA
dc.contributor.author	Abolhasan, M https://orcid.org/0000-0002-4282-6666
dc.contributor.author	Lipman, J https://orcid.org/0000-0003-2877-1168
dc.contributor.author	Shariati, N
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Jamalipour, A
dc.date.accessioned	2024-08-07T04:55:19Z
dc.date.available	2024-08-07T04:55:19Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Transactions on Cognitive Communications and Networking, 2024, PP, (99), pp. 1-1
dc.identifier.issn	2372-2045
dc.identifier.issn	2332-7731
dc.identifier.uri	http://hdl.handle.net/10453/180248
dc.description.abstract	Meeting the diverse quality-of-service (QoS) requirements in ultra-dense Internet of Things (IoT) networks operating under varying network loads is challenging. Moreover, latency-critical IoT applications cannot afford excessive control signaling overheads caused by centralized access control methods. A distributed network access approach can potentially address this problem. In this regard, multi-agent multi-armed bandit (MAB) learning is a promising tool for designing distributed network access protocols. This paper proposes a multi-agent MAB learning-based grant-free access mechanism for ultra-dense networks, where multiple base stations (BSs) serve massive delay-sensitive and delay-tolerant IoT devices. Delay-sensitive devices are prioritized to choose the BSs with larger numbers of channels in a probabilistic manner. The proposed mechanism enables the devices to improve their BS selection over time to accommodate the maximum number of devices that can meet a prescribed latency-reliability criterion. Simulation results show that the proposed MAB learning-based network access mechanism outperforms the random BS selection strategy in which end devices do not employ any learning scheme to adapt to the network dynamics.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Cognitive Communications and Networking
dc.relation.isbasedon	10.1109/TCCN.2024.3366908
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4604 Cybersecurity and privacy
dc.subject.classification	4606 Distributed computing and systems software
dc.title	Multi-Agent Multi-Armed Bandit Learning for Grant-Free Access in Ultra-Dense IoT Networks
dc.type	Journal Article
utslib.citation.volume	PP
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/Strength - TRC - Transport Research Centre
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Global Big Data Technologies Research Centre (GBDTC)
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T04:55:17Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Meeting the diverse quality-of-service (QoS) requirements in ultra-dense Internet of Things (IoT) networks operating under varying network loads is challenging. Moreover, latency-critical IoT applications cannot afford excessive control signaling overheads caused by centralized access control methods. A distributed network access approach can potentially address this problem. In this regard, multi-agent multi-armed bandit (MAB) learning is a promising tool for designing distributed network access protocols. This paper proposes a multi-agent MAB learning-based grant-free access mechanism for ultra-dense networks, where multiple base stations (BSs) serve massive delay-sensitive and delay-tolerant IoT devices. Delay-sensitive devices are prioritized to choose the BSs with larger numbers of channels in a probabilistic manner. The proposed mechanism enables the devices to improve their BS selection over time to accommodate the maximum number of devices that can meet a prescribed latency-reliability criterion. Simulation results show that the proposed MAB learning-based network access mechanism outperforms the random BS selection strategy in which end devices do not employ any learning scheme to adapt to the network dynamics.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180248