期刊
IEEE TRANSACTIONS ON COMMUNICATIONS
卷 69, 期 4, 页码 2660-2672出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCOMM.2020.3044616
关键词
Sensors; Radar; Downlink; Unmanned aerial vehicles; Radar detection; Optimization; Power control; Multi-UAV network; dual-functional radar-communication; user association; power control; joint communication and radar sensing
资金
- National Natural Science Foundation of China [61871032]
This paper investigates the network utility maximization problem in a dual-functional radar-communication multi-UAV network, proposing a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems and introducing three mechanisms based on spectral clustering, coalition game, and successive convex approximation. The proposed algorithm significantly improves the minimum user data rate and fairness of the network while increasing network utility with lower power consumption and similar localization accuracy compared to conventional techniques.
In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.
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