Jiahui Li
Jilin University
72 Papers
Jiahui Li is an academic researcher from Jilin University. The author has contributed to research in topics: Computer science & Beamforming. The author has an hindex of 2, co-authored 6 publications.
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Papers
Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach
TL;DR: A joint feature selection problem to reduce the number of the selected features while enhancing the accuracy is formulated and an improved binary particle swarm optimization (IBPSO) algorithm is proposed to solve the problem.
Secure and Energy-Efficient UAV Relay Communications Exploiting Collaborative Beamforming
TL;DR: In this article , a secure and energy-efficient communication multi-objective optimization problem (SECMOP) was formulated to circumvent the effects of the known and unknown eavesdroppers and minimize the propulsion energy consumption of UAVs, by optimizing the hovering positions and excitation current weights of the UAV and the scheduling for communicating with the remote ground users.
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Physical Layer Secure Communications Based on Collaborative Beamforming for UAV Networks: A Multi-objective Optimization Approach
Jiahui Li,Hui Kang,Geng Sun,Shuang Liang,Yanheng Liu,Ying Zhang +5 more
- 10 May 2021
TL;DR: In this article, a secure communication multi-objective optimization problem (MOP) was formulated to simultaneously improve the total secrecy rates, total maximum sidelobe levels (SLLs), and total motion energy consumptions of UAVs by jointly optimizing the positions and excitation current weights.
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Multi-Objective Optimization Approaches for Physical Layer Secure Communications Based on Collaborative Beamforming in UAV Networks
TL;DR: In this paper , the authors considered to construct a virtual antenna array consisting of UAV elements and use collaborative beamforming (CB) to achieve the UAV secure communications with different base stations (BSs), subject to the known and unknown eavesdroppers on the ground.
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Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays
TL;DR: A multi-objective optimization problem (MOP) to simultaneously minimize the mission completion time, signal strength towards the eavesdropper, and total energy cost of the UAVs is formulated and it is proved that the formulated MOP is an NP-hard, mixed-variable optimization, and large-scale optimization problem.