Publication: Diffusion model based resource allocation strategy in ultra-reliable wireless networked control systems
dc.contributor.department | Department of Electrical and Electronics Engineering | |
dc.contributor.department | Graduate School of Sciences and Engineering | |
dc.contributor.kuauthor | Ergen, Sinem Çöleri | |
dc.contributor.kuauthor | Darabi, Amirhassan Babazadeh | |
dc.contributor.schoolcollegeinstitute | College of Engineering | |
dc.contributor.schoolcollegeinstitute | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
dc.date.accessioned | 2025-03-06T20:58:31Z | |
dc.date.issued | 2024 | |
dc.description.abstract | Diffusion models offer a promising alternative to Deep Reinforcement Learning (DRL) for resource allocation in wireless networks due to their capability to model complex data distributions with greater accuracy, yet their potential remains largely unexplored. This paper proposes a diffusion model-based approach for Wireless Networked Control Systems (WNCSs) to minimize power consumption by optimizing the sampling period, blocklength, and packet error probability within the finite blocklength regime. The problem is simplified to optimizing blocklength through optimality conditions, and a dataset of channel gains and optimal blocklengths is generated via an optimization theory-based solution. The Denoising Diffusion Probabilistic Model (DDPM) is employed to generate optimal blocklength values, conditioned on channel state information (CSI). The core idea is to train the diffusion model to generate blocklength values from noise, essentially replicating the process by which the optimization solution is derived. Extensive simulations reveal that the proposed approach surpasses existing DRL-based methods, achieving near-optimal performance in terms of total power consumption. Additionally, the proposed method reduces critical constraint violations by up to eighteen times, further highlighting the enhanced accuracy of the solution. © 1997-2012 IEEE. | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | TÜBİTAK | |
dc.description.sponsorship | A. B. Darabi and S. Coleri are with the Department of Electrical and Electronics Engineering, Koc University, Istanbul, e-mail: adarabi22, scoleri@ ku.edu.tr. Sinem Coleri acknowledges the support of the Scientific and Technological Research Council of Turkey 2247-A National Leaders Research Grant #121C314 and Ford Otosan. | |
dc.identifier.doi | 10.1109/LCOMM.2024.3499745 | |
dc.identifier.grantno | Ford Otomotiv Sanayi; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 121C314; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK | |
dc.identifier.issn | 1089-7798 | |
dc.identifier.issue | 1 | |
dc.identifier.quartile | Q2 | |
dc.identifier.scopus | 2-s2.0-85209918546 | |
dc.identifier.uri | https://doi.org/10.1109/LCOMM.2024.3499745 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/27480 | |
dc.identifier.volume | 29 | |
dc.identifier.wos | 1395715400020 | |
dc.keywords | Diffusion models | |
dc.keywords | Generative ai | |
dc.keywords | Resource allocation | |
dc.keywords | Ultra-reliable low latency communication | |
dc.keywords | Wireless networked control systems | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.relation.ispartof | IEEE Communications Letters | |
dc.subject | Electrical and electronics engineering | |
dc.title | Diffusion model based resource allocation strategy in ultra-reliable wireless networked control systems | |
dc.type | Journal Article | |
dspace.entity.type | Publication | |
local.publication.orgunit1 | College of Engineering | |
local.publication.orgunit1 | GRADUATE SCHOOL OF SCIENCES AND ENGINEERING | |
local.publication.orgunit2 | Department of Electrical and Electronics Engineering | |
local.publication.orgunit2 | Graduate School of Sciences and Engineering | |
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relation.isOrgUnitOfPublication.latestForDiscovery | 21598063-a7c5-420d-91ba-0cc9b2db0ea0 | |
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