UAV Coverage Path Planning With Quantum-Based Recurrent Deep Deterministic Policy Gradient
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Silvirianti | - |
dc.contributor.author | Narottama, Bhaskara | - |
dc.contributor.author | Shin, Soo Young | - |
dc.date.accessioned | 2024-07-19T02:30:30Z | - |
dc.date.available | 2024-07-19T02:30:30Z | - |
dc.date.issued | 2024-05 | - |
dc.identifier.issn | 0018-9545 | - |
dc.identifier.issn | 1939-9359 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28805 | - |
dc.description.abstract | This study proposes quantum-based deep deterministic policy gradient (Q-DDPG) and quantum-based recurrent DDPG (Q-RDDPG) schemes for time-series optimization in UAV communications. Herein, Q-DDPG-based actor-critic reinforcement learning is utilized to optimize action selections in a large state and continuous action space. In this scheme, quantum models are exploited to reduce computational complexity and training loss. As a particular case, Q-DDPG and Q-RDDPG are employed for trajectory optimization and dynamic resource allocation in UAV communications. The results demonstrate that Q-DDPG and Q-RDDPG schemes achieved higher rewards with lower training losses compared to classical DDPG. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | UAV Coverage Path Planning With Quantum-Based Recurrent Deep Deterministic Policy Gradient | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/TVT.2023.3347219 | - |
dc.identifier.scopusid | 2-s2.0-85181578178 | - |
dc.identifier.wosid | 001224392800002 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.73, no.5, pp 7424 - 7429 | - |
dc.citation.title | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | - |
dc.citation.volume | 73 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 7424 | - |
dc.citation.endPage | 7429 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalResearchArea | Transportation | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Transportation Science & Technology | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordAuthor | Autonomous aerial vehicles | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Optimization | - |
dc.subject.keywordAuthor | NOMA | - |
dc.subject.keywordAuthor | Encoding | - |
dc.subject.keywordAuthor | Vehicle dynamics | - |
dc.subject.keywordAuthor | Resource management | - |
dc.subject.keywordAuthor | Deep deterministic policy gradient | - |
dc.subject.keywordAuthor | energy efficiency | - |
dc.subject.keywordAuthor | quantum embedding | - |
dc.subject.keywordAuthor | recurrent | - |
dc.subject.keywordAuthor | UAV communications | - |
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