Synthesis of Multiband Frequency Selective Surfaces Using Machine Learning With the Decision Tree Algorithm

Abstract

This paper presents the synthesis of multiband frequency selective surfaces (FSSs) using supervised machine learning (ML) with the decision tree (DT) algorithm. The proposed FSS structure is composed of an array of metallic patches printed on a dielectric substrate for stopband spatial filtering microwave applications. The shapes of the metallic patches are based on the sunflower (helianthus annus) geometry. In the first step, a parametric analysis is performed to investigate the use of different FSS geometries, including those with circular, annular and corolla integrated patch elements, to compose the sunflower geometry, regarding multiband and polarization independent performances with size reduction. Two bioinspired FSS geometries are synthesized using supervised machine learning with the decision tree algorithm. The random forest (RF) algorithm is used to validate the decision tree algorithm and to confirm the obtained results. The numerical analysis of the proposed FSS geometries is performed using Ansoft Designer software. Prototypes are fabricated and measured. The good agreement observed between simulated and measured results has validated the proposed approach. The use of supervised machine learning with the decision tree algorithm resulted in a particularly efficient and accurate synthesis procedure due to its intuitive implementation and simplified and effective data analysis modelling.

An Efficient Self-Tuning Proposal Distribution for Random Variate Generation With Complex Density Representation

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Triple band frequency selective surface design for 5G mm‐wave communication with artificial neural networks

“…A comparison between qualitative and quantitative parameters of FSS synthesis reported in literature is provided in Ref. 9. To this end, machine learning based synthesis methodology has not been applied to FSS design considering 5G frequencies.…”

“…Potential development of 5G systems requires spatial filters with stringent spectrum requirements 6 . High‐performance FSS filters are at the center of the research field of 5G mobile communication and in many system applications, such as radome, radar, satellite, wireless communication, chipless RFID, and reflector surfaces for antennas to enhance radiation characteristics 7–11 . In the literature, multi‐band spatial FSS filter design is an active research area considering the challenges in signal selectivity of 5G mm‐wave communication bands.…”

“…6 High-performance FSS filters are at the center of the research field of 5G mobile communication and in many system applications, such as radome, radar, satellite, wireless communication, chipless RFID, and reflector surfaces for antennas to enhance radiation characteristics. [7][8][9][10][11] In the literature, multi-band spatial FSS filter design is an active research area considering the challenges in signal selectivity of 5G mm-wave communication bands. A dual band multilayer band-pass FSS filter is presented for 5G signal filtering at 28.1 and 39.5 GHz bands.…”