Joint resource allocation and power control for radar interference mitigation in multi-UAV networks

Wang, X; Fei, Z; Huang, J; Zhang, JA; Yuan, J

Joint resource allocation and power control for radar interference mitigation in multi-UAV networks

Wang, X Fei, Z Huang, J Zhang, JA Yuan, J

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Publisher:: SCIENCE PRESS
Publication Type:: Journal Article
Citation:: Science China Information Sciences, 2021, 64, (8)
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Wang, X
dc.contributor.author	Fei, Z
dc.contributor.author	Huang, J
dc.contributor.author	Zhang, JA
dc.contributor.author	Yuan, J
dc.date.accessioned	2022-01-28T00:01:38Z
dc.date.available	2022-01-28T00:01:38Z
dc.date.issued	2021-08-01
dc.identifier.citation	Science China Information Sciences, 2021, 64, (8)
dc.identifier.issn	1674-733X
dc.identifier.issn	1869-1919
dc.identifier.uri	http://hdl.handle.net/10453/153689
dc.description.abstract	Navigation problems of unmanned air vehicles (UAVs) flying in a formation have been investigated recently, where collision avoidance is a significant issue to be addressed. In this paper, we study resource allocation and power control for radar sensing in a multi- unmanned aerial vehicle (multi-UAV) formation flight system where multiple UAVs simultaneously perform radar sensing. To cope with mutual radar interference among the UAVs, we formulate a joint channel allocation and UAV transmission power control problem to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of the radar echo signals. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into two sub-problems, i.e., channel allocation and transmission power control. An iterative channel allocation and power control algorithm (ICAPCA) is proposed to jointly solve these two sub-problems. We also propose a reduced-complexity greedy channel allocation algorithm (GCAA), which can also be used to provide an initial solution to ICAPCA. Simulation results show that the proposed ICAPCA and GCAA can improve the minimum SINR and radar sensing performance significantly.
dc.language	English
dc.publisher	SCIENCE PRESS
dc.relation.ispartof	Science China Information Sciences
dc.relation.isbasedon	10.1007/s11432-020-3133-x
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0804 Data Format, 0806 Information Systems, 0899 Other Information and Computing Sciences
dc.subject.classification	Software Engineering
dc.title	Joint resource allocation and power control for radar interference mitigation in multi-UAV networks
dc.type	Journal Article
utslib.citation.volume	64
utslib.for	0804 Data Format
utslib.for	0806 Information Systems
utslib.for	0899 Other Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-08-01T00:00:00+1000Z
dc.date.updated	2022-01-28T00:01:37Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	64
utslib.citation.issue	8

Abstract:

Navigation problems of unmanned air vehicles (UAVs) flying in a formation have been investigated recently, where collision avoidance is a significant issue to be addressed. In this paper, we study resource allocation and power control for radar sensing in a multi- unmanned aerial vehicle (multi-UAV) formation flight system where multiple UAVs simultaneously perform radar sensing. To cope with mutual radar interference among the UAVs, we formulate a joint channel allocation and UAV transmission power control problem to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of the radar echo signals. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into two sub-problems, i.e., channel allocation and transmission power control. An iterative channel allocation and power control algorithm (ICAPCA) is proposed to jointly solve these two sub-problems. We also propose a reduced-complexity greedy channel allocation algorithm (GCAA), which can also be used to provide an initial solution to ICAPCA. Simulation results show that the proposed ICAPCA and GCAA can improve the minimum SINR and radar sensing performance significantly.

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http://hdl.handle.net/10453/153689