Publication:
Optimization theory based deep reinforcement learning for resource allocation in ultra-reliable wireless networked control systems

dc.contributor.coauthor 
dc.contributor.departmentDepartment of Electrical and Electronics Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.kuauthorAli, Hamida Qumber
dc.contributor.kuauthorDarabi, Amirhassan Babazadeh
dc.contributor.kuauthorErgen, Sinem Çöleri
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.date.accessioned2024-12-29T09:37:53Z
dc.date.issued2024
dc.description.abstractThe design of Wireless Networked Control System (WNCS) requires addressing critical interactions between control and communication systems with minimal complexity and communication overhead while providing ultra-high reliability. This paper introduces a novel optimization theory based deep reinforcement learning (DRL) framework for the joint design of controller and communication systems. The objective of minimum power consumption is targeted while satisfying the schedulability and rate constraints of the communication system in the finite blocklength regime and stability constraint of the control system. Decision variables include the sampling period in the control system, and blocklength and packet error probability in the communication system. The proposed framework contains two stages: optimization theory and DRL. In the optimization theory stage, following the formulation of the joint optimization problem, optimality conditions are derived to find the mathematical relations between the optimal values of the decision variables. These relations allow the decomposition of the problem into multiple building blocks. In the DRL stage, the blocks that are simplified but not tractable are replaced by DRL. Via extensive simulations, the proposed optimization theory based DRL approach is demonstrated to outperform the optimization theory and pure DRL based approaches, with close to optimal performance and much lower complexity. IEEE
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.issue8
dc.description.openaccessAll Open Access
dc.description.openaccessGreen Open Access
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipScientific and Technological Research Council of Turkey 2247-ANational Leaders Research Grant
dc.description.volume72
dc.identifier.doi10.1109/TCOMM.2024.3381712
dc.identifier.eissn1558-0857
dc.identifier.issn0090-6778
dc.identifier.link 
dc.identifier.quartileQ1
dc.identifier.scopus2-s2.0-85189295174
dc.identifier.urihttps://doi.org/10.1109/TCOMM.2024.3381712
dc.identifier.urihttps://hdl.handle.net/20.500.14288/22487
dc.identifier.wos1294594400023
dc.keywordsCommunication systems
dc.keywordsControl systems
dc.keywordsDeep reinforcement learning
dc.keywordsDelays
dc.keywordsError probability
dc.keywordsFinite blocklength
dc.keywordsMathematical models
dc.keywordsOptimization
dc.keywordsOptimization theory
dc.keywordsResource allocation
dc.keywordsResource management
dc.keywordsUltra-reliable low latency communication
dc.keywordsWireless networked control systems
dc.language.isoeng
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.grantno 
dc.relation.ispartofIEEE Transactions on Communications
dc.rights 
dc.subjectElectrical engineering
dc.subjectElectronic engineering
dc.subjectTelecommunications
dc.titleOptimization theory based deep reinforcement learning for resource allocation in ultra-reliable wireless networked control systems
dc.typeJournal Article
dc.type.other 
dspace.entity.typePublication
local.contributor.kuauthorAli, Hamida Qumber
local.contributor.kuauthorDarabi, Amirhassan Babazadeh
local.contributor.kuauthorErgen, Sinem Çöleri
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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relation.isOrgUnitOfPublication3fc31c89-e803-4eb1-af6b-6258bc42c3d8
relation.isOrgUnitOfPublication.latestForDiscovery21598063-a7c5-420d-91ba-0cc9b2db0ea0
relation.isParentOrgUnitOfPublication8e756b23-2d4a-4ce8-b1b3-62c794a8c164
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