2019
DOI: 10.1049/iet-map.2019.0060
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3D‐MIMO beamforming realised by AQPSO algorithm for cylindrical conformal phased array

Abstract: Three‐dimensional (3D)‐MIMO beamforming realised by adaptive quantum particle swarm optimisation (AQPSO) algorithm for the 36‐element cylindrical conformal phased array is proposed in this study. The cylindrical conformal phased array consists of 36 circularly polarised microstrip antenna elements with ultra‐wideband and high gain characteristics. The conformal array is divided into 12 subarrays using reconfigurable technology, and each subarray produces six beams to cover the corresponding spatial area. The 3… Show more

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Cited by 4 publications
(2 citation statements)
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“…In 2018, Cummings et al proposed the information theory performance indicators of narrow-band STAR imaging arrays and divided these arrays into transmit and receive apertures through detailed testing of possible partitions and the application of GA [15,16]. In beamforming, many studies prove successful cases of swarm intelligence algorithms optimizing beamforming to improve phased array performance, such as beamforming-based pattern synthesis [17]. Reference [18] proposed an enhanced ACO algorithm to optimize the beam pointing position, beam width, and side lobes of the antenna array, which has competitive advantages over other algorithms.…”
Section: Introductionmentioning
confidence: 99%
“…In 2018, Cummings et al proposed the information theory performance indicators of narrow-band STAR imaging arrays and divided these arrays into transmit and receive apertures through detailed testing of possible partitions and the application of GA [15,16]. In beamforming, many studies prove successful cases of swarm intelligence algorithms optimizing beamforming to improve phased array performance, such as beamforming-based pattern synthesis [17]. Reference [18] proposed an enhanced ACO algorithm to optimize the beam pointing position, beam width, and side lobes of the antenna array, which has competitive advantages over other algorithms.…”
Section: Introductionmentioning
confidence: 99%
“…We found that the swarm intelligence optimization methods have the potential to achieve these goals. They have been successful in the fields such as beamforming-based pattern synthesis [ 12 ], array optimization [ 13 , 14 ], DC brushless motor efficiency problems [ 15 ], Loney’s solenoid problem [ 16 ], and stock index forecasting [ 17 ]. Extensive literature reveals that compared to traditional particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), the brainstorm optimization (BSO) algorithm [ 18 ] has the characteristics of fast convergence, excellent robustness, and a strong global optimization ability in solving non-convex, multi-objective, and multi-modal optimization problems.…”
Section: Introductionmentioning
confidence: 99%