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Hardware impairment-aware data collection and wireless power transfer using a MIMO full-duplex UAV

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728174402
DOIs
PublishedJun 2020
Event2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Dublin, Ireland
Duration: 7 Jun 202011 Jun 2020

Publication series

Name2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings

Conference

Conference2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020
CountryIreland
CityDublin
Period7/06/202011/06/2020

King's Authors

Abstract

In this paper, we study a hardware impairment-aware data collection and wireless power transfer (WPT) framework using a multiple-input multiple-output (MIMO) full-duplex (FD) unmanned aerial vehicle (UAV). To enable high energy saving of ground users, we allow a MIMO FD UAV charge battery life time limited ground users while at the same time collect data from them in a flying-and-hovering mode. With the aim of practical implementation, we formulate an energy minimization problem by taking the UAV hardware impairments, its flying-and-hovering time, the number of uploaded data bits, and the energy harvesting causality into account. Due to non-convexity of the problem in terms of UAV trajectory and transmit beamforming for WPT, tracking the global optimality is quite challenging. Alternatively, we exploit a local optimal solution by implementing the proposed fixed-point search combining with successive convex approximation algorithm. In comparison to benchmark schemes, our results illustrate superior performances especially when the hardware impairment effects are taken into account. In addition, the proposed iterative algorithm exhibits fast convergence and fairly low computational complexity.

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