Lateral confinement of high-impedance surface-waves through reinforcement learning

• Published in: Electronics letters 2020-11, Vol.56 (23), p.1262-1264
• Main Authors: Morocho-Cayamcela, M.E, Lim, W
• Format: Article
• Language: English
• Subjects: artificially structured material; constrained path; electromagnetic wave; electromagnetic wave propagation; high‐impedance surface‐waves; iterative methods; iterative model; lateral confinement; learned‐pattern; learning (artificial intelligence); materials science computing; metamaterials; metasurface; Microwave technology; model‐free policy‐based reinforcement learning model; optimal patch pattern; patch sizes; penalties; surface electromagnetic waves; surface wave radiate
• ISSN: 0013-5194; 1350-911X; 1350-911X
• DOI: 10.1049/el.2020.1977

The authors present a model-free policy-based reinforcement learning model that introduces perturbations on the pattern of a metasurface. The objective is to learn a policy that changes the size of the patches, and therefore the impedance in the sides of an artificially structured material. The proposed iterative model assigns the highest reward when the patch sizes allow the transmission along a constrained path and penalties when the patch sizes make the surface wave radiate to the sides of the metamaterial. After convergence, the proposed model learns an optimal patch pattern that achieves lateral confinement along the metasurface. Simulation results show that the proposed learned-pattern can effectively guide the electromagnetic wave through a metasurface, maintaining its instantaneous eigenstate when the homogeneity is perturbed. Moreover, the pattern learned to prevent reflections by changing the patch sizes adiabatically. The reflection coefficient ${\bi S}_{1\comma\;2}$S1,2 shows that most of the power gets transferred from the source to the destination with the proposed design.