Please use this identifier to cite or link to this item: https://doi.org/10.1109/TWC.2019.2911939
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dc.title3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting
dc.contributor.authorYou, Changsheng
dc.contributor.authorZhang, Rui
dc.date.accessioned2019-07-18T08:38:33Z
dc.date.available2019-07-18T08:38:33Z
dc.date.issued2019-06-01
dc.identifier.citationYou, Changsheng, Zhang, Rui (2019-06-01). 3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 18 (6) : 3192-3207. ScholarBank@NUS Repository. https://doi.org/10.1109/TWC.2019.2911939
dc.identifier.issn1536-1276
dc.identifier.issn1558-2248
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/156708
dc.description.abstract© 2002-2012 IEEE. Dispatching unmanned aerial vehicles (UAVs) to harvest sensing-data from distributed sensors is expected to significantly improve the data collection efficiency in conventional wireless sensor networks (WSNs). In this paper, we consider a UAV-enabled WSN, where a flying UAV is employed to collect data from multiple sensor nodes (SNs). Our objective is to maximize the minimum average data collection rate from all SNs subject to a prescribed reliability constraint for each SN by jointly optimizing the UAV communication scheduling and three-dimensional (3D) trajectory. Different from the existing works that assume the simplified line-of-sight (LoS) UAV-ground channels, we consider the more practically accurate angle-dependent Rician fading channels between the UAV and SNs with the Rician factors determined by the corresponding UAV-SN elevation angles. However, the formulated optimization problem is intractable due to the lack of a closed-form expression for a key parameter termed effective fading power that characterizes the achievable rate given the reliability requirement in terms of outage probability. To tackle this difficulty, we first approximate the parameter by a logistic ('S' shape) function with respect to the 3D UAV trajectory by using the data regression method. Then, the original problem is reformulated to an approximate form, which, however, is still challenging to solve due to its non-convexity. As such, we further propose an efficient algorithm to derive its suboptimal solution by using the block coordinate descent technique, which iteratively optimizes the communication scheduling, the UAV's horizontal trajectory, and its vertical trajectory. The latter two subproblems are shown to be non-convex, while locally optimal solutions are obtained for them by using the successive convex approximation technique. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithm and draw new insights on the 3D UAV trajectory under the Rician fading as compared to conventional LoS channel models.
dc.language.isoen
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.sourceElements
dc.subjectScience & Technology
dc.subjectTechnology
dc.subjectEngineering, Electrical & Electronic
dc.subjectTelecommunications
dc.subjectEngineering
dc.subjectUAV communication
dc.subjectwireless sensor network
dc.subject3D trajectory optimization
dc.subjectlogistic function
dc.subjectdata regression
dc.subjectUNMANNED AERIAL VEHICLES
dc.subjectCOMMUNICATION
dc.subjectDESIGN
dc.subjectALTITUDE
dc.subjectCOVERAGE
dc.typeArticle
dc.date.updated2019-07-18T08:11:44Z
dc.contributor.departmentDEPT OF ELECTRICAL & COMPUTER ENGG
dc.description.doi10.1109/TWC.2019.2911939
dc.description.sourcetitleIEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
dc.description.volume18
dc.description.issue6
dc.description.page3192-3207
dc.published.statePublished
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