Delay-Aware Link Scheduling in IAB Networks with Dynamic User Demands

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dc.contributor.authorSadovaya, Yekaterinacs
dc.contributor.authorVikhrova, Olgacs
dc.contributor.authorMao, Weics
dc.contributor.authorYeh, Shu-Pingcs
dc.contributor.authorSemiari, Omidcs
dc.contributor.authorNikopour, Hoseincs
dc.contributor.authorTalwar, Shilpacs
dc.contributor.authorAndreev, Sergeycs
dc.coverage.issue10cs
dc.coverage.volume73cs
dc.date.accessioned2025-02-03T14:42:45Z
dc.date.available2025-02-03T14:42:45Z
dc.date.issued2024-06-21cs
dc.description.abstractIntegrated Access and Backhaul (IAB) is a costefficient network densification technology for improving the coverage and capacity of the millimeter-wave (mmWave) cellular networks. In IAB systems, user traffic is forwarded to/from the wired base station by one or more relay stations, known as IAB nodes. Due to the multi-hop relaying, these systems may be subject to large packet delays and poor performance when the load is unevenly distributed among nodes. Addressing this limitation via delay-aware access and backhaul link scheduling in IAB networks is challenging due to potentially large network scale, complex topology, half-duplex, and interference constraints. In this paper, the topical link scheduling problem is formulated as a Markov decision problem (MDP) for a single-donor IAB system with a general topology that allows for users with different delay requirements and traffic dynamics. The proposed link scheduling strategy jointly optimizes (i) user traffic routing and (ii) multiplexing of access and backhaul links under half-duplex constraints and non-negligible interference that may arise in dense IAB systems even with high beam directionality. To address the complexity of our formulated MDP, we consider several approximation methods, namely, Q-learning, Monte Carlo Tree Search (MCTS), and genetic algorithms (GAs). Then, we propose a customized version of the GA, which provides the preferred optimality-complexity trade-off and offers a 15% packet delay reduction as compared to the state-of-the-art backpressure algorithm.en
dc.formattextcs
dc.format.extent15125-15139cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY. 2024, vol. 73, issue 10, p. 15125-15139.en
dc.identifier.doi10.1109/TVT.2024.3409179cs
dc.identifier.issn1939-9359cs
dc.identifier.other189106cs
dc.identifier.urihttps://hdl.handle.net/11012/249919
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE TRANSACTIONS ON VEHICULAR TECHNOLOGYcs
dc.relation.urihttps://ieeexplore.ieee.org/document/10568354/cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1939-9359/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectIABen
dc.subjectmillimeter-waveen
dc.subjectlink schedulingen
dc.subjectroutingen
dc.subjecthalf-duplex constrainten
dc.subjectinterferenceen
dc.subjectuser dynamicsen
dc.titleDelay-Aware Link Scheduling in IAB Networks with Dynamic User Demandsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-189106en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:42:45en
sync.item.modts2025.01.31 11:32:04en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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