Joint Quantum Reinforcement Learning and Stabilized Control for Spatio-Temporal Coordination in Metaverse
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Soohyun | - |
dc.contributor.author | Chung, Jaehyun | - |
dc.contributor.author | Park, Chanyoung | - |
dc.contributor.author | Jung, Soyi | - |
dc.contributor.author | Choi, Minseok | - |
dc.contributor.author | Cho, Sungrae | - |
dc.contributor.author | Kim, Joongheon | - |
dc.date.accessioned | 2024-06-25T03:00:39Z | - |
dc.date.available | 2024-06-25T03:00:39Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 1536-1233 | - |
dc.identifier.issn | 1558-0660 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/74359 | - |
dc.description.abstract | In order to build realistic metaverse systems, enabling high synchronization between physical-space and virtual meta-space is essentially required. For this purpose, this paper proposes a novel system-wide coordination algorithm for high synchronization under characteristics (<italic>i.e.</italic>, highly realistic meta-space construction under the constraints of physical-space). The proposed algorithm consists of the following three stages. The first stage is quantum multi-agent reinforcement learning (QMARL)-based scheduling for low-delay temporal-synchronization using differentiated age-of-information (AoI) during data gathering in physical-space by observers for meta-space construction. This is beneficial for scalability according to action dimension reduction in reinforcement learning computation. The second stage is for creating virtual contents under delay constraints in meta-space based on the gathered data. When rendering regions that have received more user attention, avatar-popularity is considered for spatio-synchronization. Thus, a stabilized control mechanism is designed for time-average reality quality maximization for each region. The last stage is for caching based on avatar-popularity and AoI which can be helpful in constructing low-delay realistic meta-space. Furthermore, the concept of AoI is divided into two separate sub-concepts of physical AoI and virtual AoI such that the AoI in virtual meta-space can be thoroughly implemented. IEEE | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Joint Quantum Reinforcement Learning and Stabilized Control for Spatio-Temporal Coordination in Metaverse | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TMC.2024.3407883 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Mobile Computing | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85194817407 | - |
dc.citation.title | IEEE Transactions on Mobile Computing | - |
dc.type.docType | Article in press | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Age-of-Information | - |
dc.subject.keywordAuthor | Avatars | - |
dc.subject.keywordAuthor | Metaverse | - |
dc.subject.keywordAuthor | Metaverse | - |
dc.subject.keywordAuthor | Observers | - |
dc.subject.keywordAuthor | Quantum computing | - |
dc.subject.keywordAuthor | Quantum Reinforcement Learning | - |
dc.subject.keywordAuthor | Reinforcement learning | - |
dc.subject.keywordAuthor | Servers | - |
dc.subject.keywordAuthor | Synchronization | - |
dc.subject.keywordAuthor | Synchronization | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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