Publication:
Diffusion model based resource allocation strategy in ultra-reliable wireless networked control systems

dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorErgen, Sinem Çöleri
dc.contributor.kuauthorDarabi, Amirhassan Babazadeh
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2025-03-06T20:58:31Z
dc.date.issued2024
dc.description.abstractDiffusion 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.indexedbyWOS
dc.description.indexedbyScopus
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuTÜBİTAK
dc.description.sponsorshipA. 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.doi10.1109/LCOMM.2024.3499745
dc.identifier.grantnoFord Otomotiv Sanayi; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 121C314; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK
dc.identifier.issn1089-7798
dc.identifier.issue1
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85209918546
dc.identifier.urihttps://doi.org/10.1109/LCOMM.2024.3499745
dc.identifier.urihttps://hdl.handle.net/20.500.14288/27480
dc.identifier.volume29
dc.identifier.wos1395715400020
dc.keywordsDiffusion models
dc.keywordsGenerative ai
dc.keywordsResource allocation
dc.keywordsUltra-reliable low latency communication
dc.keywordsWireless networked control systems
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Communications Letters
dc.subjectElectrical and electronics engineering
dc.titleDiffusion model based resource allocation strategy in ultra-reliable wireless networked control systems
dc.typeJournal Article
dspace.entity.typePublication
local.publication.orgunit1College of Engineering
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit2Department of Electrical and Electronics Engineering
local.publication.orgunit2Graduate School of Sciences and Engineering
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