Lateral confinement of high‐impedance surface‐waves through reinforcement learning. Issue 23 (25th September 2020)

• Record Type: Journal Article
• Authors: Morocho‐Cayamcela, M.E.; Lim, W.
• Is Part Of: Electronics letters. Volume 56:Issue 23(2020)
• Page Start: 1262
• Page End: 1264
• Publication Date: 2020-09-25
• Subjects: metamaterials -- learning (artificial intelligence) -- electromagnetic wave propagation -- iterative methods -- surface electromagnetic waves -- materials science computing; metasurface -- learned‐pattern -- electromagnetic wave -- patch sizes -- lateral confinement -- high‐impedance surface‐waves -- model‐free policy‐based reinforcement learning model -- artificially structured material -- iterative model -- constrained path -- penalties -- surface wave radiate -- optimal patch pattern; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2020.1977
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : 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 S 1, 2 shows that most of the power gets transferred from the source to the destination with the proposed design.