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Double deep Q-learn...
Double deep Q-learning network-based path planning in UAV-assisted wireless powered NOMA communication networks
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- Lei, Ming (author)
- School of Computer Science, Shaanxi Normal University, Xi'an, China
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- Fowler, Scott, 1970- (author)
- Linköpings universitet,Kommunikations- och transportsystem,Tekniska fakulteten
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- Wang, Juzhen (author)
- Electronic Information School, Wuhan University, Wuhan, China
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- Zhang, Xingjun (author)
- Xi'an Jiaotong University, Xi'an, China
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- Yu, Bocheng (author)
- Xi'an Jiaotong University, Xi'an, China
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- Yu, Bin (author)
- School of Computer Science and Technology, Xidian University, Xi'an, China
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2021
- 2021
- English.
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In: 2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665413688 - 9781665413695 ; , s. 1-5
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- This paper studies an unmanned aerial vehicle (UAV)-enabled wireless power communication networks (WPCN-s), where the UAV provides energy for mobile user nodes (M-UNs) and receives information from M-UNs. The movement of M-UN complies with a Gauss-Markov random model. To ensure acceptable quality-of-service (QoS), we consider dynamically planning the flight path of the UAV according to the movements of M-UNs. Since the flight time of UAV is restricted by limited energy, nonorthogonal multiple access (NOMA) is adopted to access a large number of M-UNs for simultaneous information transmission. Based on the above considerations, we aim to maximize the throughput via path planning of the UAV, subject to the QoS requirements of M-UNs and the UAV's energy constraint. To handle the challenges brought by dynamically changing channels to solving the problem, we propose a QoS-based double deep Q-learning network (DDQN). Numerical simulation results show that, compared with the conventional algorithms, the proposed framework achieves higher throughput.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
Keyword
- WPCN; Gauss-Markov random model; non-orthogonal multiple access; path planning; DDQN
Publication and Content Type
- ref (subject category)
- kon (subject category)
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