Optimization of planar thinned antenna array based on genetic and convex hybrid algorithm

Fu, Yu; Guo, Zhi-Gui; Wang, Haowen; Gui, Yunsong; Yang, Guo‐Min

doi:10.1109/piers.2016.7734641

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Considering the limitations of single UAV communication, some works have also utilized CB technology to synthesize planar VAA to extend UAV communication time and improve its application range, and using SI optimization algorithms to reduce SLLs and communication delay, as well as improving transmission rate and antenna gain. Fu et al 22 propose a hybrid algorithm combining GA and convex optimization to reduce the SLL and communication delay of planar VAA. El‐Khamy et al 23 use a hybrid method that combines ant colony optimization (ACO) algorithm and the least mean square (LMS) algorithm to achieve better communication performance by optimizing the beam pattern.…”

Section: Related Workmentioning

confidence: 99%

A multi‐objective optimization approach for beam pattern synthesis ofUAVvirtual rectangular antenna array

Mei,

Guo,

Kang

et al. 2024

Int J Numerical Modelling

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Virtual antenna array (VAA) formed by unmanned aerial vehicle (UAV) antenna units using collaborative beamforming (CB) technology plays an important role in the air communication system, and can be used in radar, military, disaster rescue and other places. However, there are still some issues with the beam pattern formed by this method, such as high sidelobe level (SLL), high cost and low efficiency. In this article, each UAV carries an omnidirectional antenna unit, and a large number of UAVs form a UAV virtual rectangular antenna array (UVRAA) to communicate with the ground base station (BS). We formulate an overhead minimization and efficient communication multi‐objective optimization problem (OMECMOP) which jointly optimize the excitation current weights of the UVRAA and reduce the number of UAVs in operation to improve the beam pattern, enhance the communication efficiency and decrease the overhead of UVRAA. In addition, we also propose an improved multi‐objective multi‐verse optimization algorithm based on the inverse decline curve type (ISDT‐MOMVO) which introduces a strategy optimization initialization solution with quasi‐opposition based learning (QBL) and a hybrid solution updating operators to solve the OMECMOP. The simulation results show that compared with other traditional swarm intelligence (SI) optimization algorithms the ISDT‐MOMVO algorithm produces better beam pattern and the thinning rate can reach 50%.

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Section: Related Workmentioning

confidence: 99%