October 11, 2024 by Compuscript Ltd
Collected at: https://techxplore.com/news/2024-10-ai-physics-meta-antennas.html
Ka-band metasurface antennas, with their low-cost, low-profile design and superior beam-steering capabilities, show significant potential in the field of satellite communications. However, the constraints of limited satellite resources and significant atmospheric losses at Ka-band frequencies require these antennas to achieve wide-angle beam scanning capabilities and high antenna gain, adding considerable complexity to their design.
In order to achieve the design of a multifunctional and highly efficient meta-antenna, the design optimization will involve numerous parameters, greatly increasing the use of computational resources and optimization time. Addressing the critical issue of balancing multiple optimization objectives, such as gain and scanning angle, while improving optimization speed, remains a key challenge in the design process.
To address these challenges of meta-antenna design, researchers from the University of Electronic Science and Technology of China, Tongji University, and City University of Hong Kong have joined forces in an extensive collaboration.
Leveraging their long-term expertise in the field of meta-optics, they proposed a Ka-band meta-antenna design method based on a Physics-Assisted Particle Swarm Optimization (PA-PSO) algorithm. Using this method, they designed and fabricated a Ka-band meta-antenna. The study is published in the journal Opto-Electronic Science.
The antenna proposed in the paper is designed using the PA-PSO algorithm. Compared to the traditional PSO algorithm, the optimization direction of particles in the PA-PSO algorithm is guided by extremum conditions derived from the variational method. This not only reduces computation time but also decreases the likelihood of finding suboptimal designs.
The final optimized results indicate that the relative strength achieved by the PA-PSO algorithm is 94.62806, which is comparable to the relative strength of 94.62786 achieved by the traditional PSO algorithm. However, the computational cost of the PA-PSO algorithm is significantly lower; it reaches the optimal state after only 650 iterations, whereas the traditional PSO algorithm requires 4100 iterations.
This means the computation time of the PA-PSO algorithm is less than one-sixth of that for the PSO algorithm. Therefore, the PA-PSO method can guide particle swarms more efficiently, reducing computation time, making it an important tool for addressing complex multivariate and multi-objective optimization challenges.
Gain profiles of the metalens antenna when the feed is placed on the focal plane with different displacements x. Comparison between the experimental results (blue lines) and simulation results (red lines) when the feed source position is (a) at x = 0, showing a maximum gain of 21.7 dBi, which corresponds to an angle of 0°; (b) at x = 15 mm, showing a maximum gain is 21.2 dBi, which corresponds to an angle of 25°; (c) at x = 30 mm, showing a maximum gain is 18.3 dBi, which corresponds to an angle of 55°. (d) The relationship between the maximum gain angles and the corresponding gains obtained from testing the feed source at different positions. Inset shows the sample photo and unit cell structure diagram. Credit: Opto-Electronic Science (2024). DOI: 10.29026/oes.2024.240014
Performance of the PA-PSO algorithm. (a) Variation of the relative electric field intensity with respect to the times of iteration for PA-PSO and PSO algorithms. The purple line shows the calculation errors. The four hexagons from bottom to top represent phase distributions at different stages: initial phase distribution, PSO algorithm iteration 650 times, PSO algorithm iteration 1500 times, and PSO algorithm iteration 4,100 times (PA-PSO algorithm iteration 650 times). (b) Comparison of FOVs and F/D for planar lens antennas. The colors of the points indicate the fluctuation of gains when scanning within the field of view range. Credit: Opto-Electronic Science (2024). DOI: 10.29026/oes.2024.240014
Based on the phase distribution optimized by the PA-PSO algorithm, the team designed and fabricated a hexagonal meta-antenna sample with a focal length of 22 mm, diagonal length of 110 mm, and a thickness of only 1.524 mm.
The antenna has an f-number of only 0.2, a beam scanning angle of ±55°, a maximum gain of 21.7 dBi, and a gain flatness of within 4 dB. This innovative hexagonal meta-antenna, with its wide scanning angle, compact design, and high transmission gain, exhibits enormous potential for applications in satellite communication, radar systems, 5G networks, and the Internet of Things, among many other fields.
More information: Shibin Jiang et al, Ka-Band metalens antenna empowered by physics-assisted particle swarm optimization (PA-PSO) algorithm, Opto-Electronic Science (2024). DOI: 10.29026/oes.2024.240014
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