Zeng, Y., Qing, X., Chia, M. Y.-W., & Hlaing, M. (2022). Design of a Non-periodic Mushroom Antenna Using Bayesian Optimization. 2022 16th European Conference on Antennas and Propagation (EuCAP). https://doi.org/10.23919/eucap53622.2022.9769060
A broadband non-periodic mushroom antenna is designed via Bayesian optimization. By tuning the length and width of the 16 patches of a 4 × 4 meta mate rial mushroom structure, the non-periodic mushroom antenna achieves a 23.95% fractional bandwidth (|S11|&lt;-10 dB) , while keeping the gain variation small at boresight direction. Bayesian optimization is particularly suitable for design and optimization with a large number of design parameters, for example, 32 design parameters in this design, since the conventional exhaustive search becomes infeasible for such a large search space. The proposed antenna is prototyped and measured, with good agreement between the simulation and measurement achieved. The proposed antenna is able to operate in the widely deployed n78 band of 5G New Radio.
This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - Industry Alignment Fund − Pre-Positioning (IAFPP)
Grant Reference no. : A1897a0054
This research / project is supported by the Agency for Science, Technology and Research (A*STAR), Singapore - AME YIRG Fund
Grant Reference no. : A2084c0173