Adaptive Polynomial Chaos Expansion for Uncertainty Quantification and Optimization of Horn Antennas at SubTHz Frequencies
<|reference_start|>Adaptive Polynomial Chaos Expansion for Uncertainty Quantification and Optimization of Horn Antennas at SubTHz Frequencies: Sub-terahertz (subTHz) antennas will play an important role in the next generations of wireless communication systems. However, when comes to the subTHz frequency spectrum, the antenna fabrication tolerance needs to be accurately considered during the design stage. The classic approach to studying the average performance of an antenna design considering fabrication tolerances is through the use of the Monte-Carlo (MC) method. In this paper, we propose an adaptive polynomial chaos expansion (PCE) method for the uncertainty quantification analysis of subTHz horn antennas with flat-top radiation patterns. The proposed method builds a surrogate model of the antenna's response to electromagnetic (EM) excitation and estimates its statistical moments with accuracy close to the reference MC method, but with a much smaller computational complexity of roughly two orders of magnitude. Moreover, the surrogate model based on PCE can substitute full-wave EM solvers in producing samples for electromagnetic quantities of interest, resulting in significant computational efficiency gains during optimization tasks. To this end, we successfully combined PCE with the particle swarm optimization method to design the free parameters of a horn antenna at $95$ GHz for a flat-top gain.<|reference_end|>
@article{papadopoulos2024adaptive,
    title={Adaptive Polynomial Chaos Expansion for Uncertainty Quantification and
  Optimization of Horn Antennas at SubTHz Frequencies},
    author={Aristeides D. Papadopoulos and Yihan Ma and Qi Luo and George C.
  Alexandropoulos},
    journal={arXiv preprint arXiv:2404.04542},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04542},
    primaryClass={cs.IT cs.ET math.IT}
}