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The design and implementation of low-profile antennas has been analyzed inpast decades from different perspectives while the purpose is to have a smallsize in the device, and an adequate electromagnetic behavior. This work pursuesa methodology to identify small antennas and consequently presents somesimilarities. Meanwhile, curved surfaces are considered for a certain varietyof antennas with reduced size. The so-called deep reinforcement learningtechnique is used as an assistance against morphological variations that arespecifically taken into account in this work. The objective is to identifyantennas that can be efficiently mounted on the surface of metal tubes such asthose frequently present in public infrastructure (e.g. traffic lights andluminaries). The motivation is to reduce the visual impact and optimize theradiation pattern of the antenna. It is analyzed that if changes in variablessuch as the radius of curvature, or the electromagnetic properties of thematerials appear, an automatic identification of the underlying characteristicsof the problem (by means of machine learning techniques) can readjust thedesign efficiently. The results obtained in this work are analyzed based onvariables that are typically used to characterize antennas, such as theirimpedance and radiation pattern.

A Reusable Framework Based on Reinforcement Learning to Design Antennas for Curved Surfaces