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This article considers a wireless-powered communication network (WPCN) composed of a multiantenna hybrid access point (HAP) based on nonlinear energy harvesting (EH). To improve some distant WDs' throughput performance, one of them is allowed to be selected as a cluster head (CH) to help transfer information from other cluster members (CMs). Nevertheless, the proposed clustering collaboration's performance is essentially restricted by the CH's energy-intensive consumption (EC), which requires to transfer every WDs' information, covering its own. In order to figure out the question, the HAP's energy beamforming (EB) capability with multiple antennas is utilized that can concentrate greater transmission power into the CH to equilibrate its EC to assist other WDs. To be specific, each WD's throughput performance is firstly derived under the proposed approach. A high-efficiency optimization algorithm for addressing cooperative optimization problem is put forward. In addition, the simulations are carried out in the actual network environment, and the results demonstrate that our proposed clustering collaboration with multiple antennas can validly enhance the WPCN's throughput fairness based on nonlinear EH. [ABSTRACT FROM AUTHOR]

Navigation plays an important role in the task execution of the micro-unmanned aerial vehicle (UAV) swarm. The Cooperative Navigation (CN) method that fuses the observation of onboard sensors and relative information between UAVs is a research hotspot. Aiming at the efficiency and accuracy problems of previous studies, this article proposes a hybrid-CN method for UAV swarm based on Factor Graph and Kalman filter. A global Factor Graph is used to combine Global Navigation Satellite System (GNSS) and ranging information to provide position estimations for modifying the distributed Kalman filter; distributed Kalman filter is established on each UAV to fuse inertial information and optimized position estimation to modify the navigation states. In order to provide time-consistent GNSS position information for the Factor Graph, a time synchronization filter is designed. The proposed method is tested and verified using standard Monte Carlo simulations, simulation results show that it can provide a more precise and efficient CN solution than traditional CN methods. [ABSTRACT FROM AUTHOR]