Show simple item record

AuthorLi, Kai
AuthorNi, Wei
AuthorTovar, Eduardo
AuthorGuizani, Mohsen
Available date2022-12-05T15:06:15Z
Publication Date2020-06-01
Publication Name2020 International Wireless Communications and Mobile Computing, IWCMC 2020
Identifierhttp://dx.doi.org/10.1109/IWCMC48107.2020.9148316
CitationLi, K., Ni, W., Tovar, E., & Guizani, M. (2020, June). Deep reinforcement learning for real-time trajectory planning in UAV networks. In 2020 International Wireless Communications and Mobile Computing (IWCMC) (pp. 958-963). IEEE.‏
ISBN9781728131290
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85089694273&origin=inward
URIhttp://hdl.handle.net/10576/36939
AbstractIn Unmanned Aerial Vehicle (UAV)-enabled wireless powered sensor networks, a UAV can be employed to charge the ground sensors remotely via Wireless Power Transfer (WPT) and collect the sensory data. This paper focuses on trajectory planning of the UAV for aerial data collection and WPT to minimize buffer overflow at the ground sensors and unsuccessful transmission due to lossy airborne channels. Consider network states of battery levels and buffer lengths of the ground sensors, channel conditions, and location of the UAV. A flight trajectory planning optimization is formulated as a Partial Observable Markov Decision Process (POMDP), where the UAV has partial observation of the network states. In practice, the UAV-enabled sensor network contains a large number of network states and actions in POMDP while the up-to-date knowledge of the network states is not available at the UAV. To address these issues, we propose an onboard deep reinforcement learning algorithm to optimize the realtime trajectory planning of the UAV given outdated knowledge on the network states.
SponsorThis work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDB/04234/2020); also by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by national funds through the FCT, within project(s) POCI-01-0145-FEDER-029074 (ARNET).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDeep reinforcement learning
Trajectory planning
Unmanned aerial vehicles
Wireless power transfer
Wireless sensor networks
TitleDeep Reinforcement Learning for Real-Time Trajectory Planning in UAV Networks
TypeConference Proceedings
Pagination958-963
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record