Spherical Mapping of the Second-Order Phoenix Cell for Unbounded Direct Reflectarray Copolar Optimization

Richard, Vincent; Loison, Renaud; Gillard, Raphaël; Legay, Hervé; Romier, Maxime; Martinaud, Jean-Paul; Bresciani, Daniele; Delepaux, and Fabien

doi:10.2528/pierc18102303

Cited by 18 publications

(29 citation statements)

References 30 publications

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“…It is worth mentioning that the proposed model is intended to provide a very fast and qualitative tool for pre-dimensioning studies of a large set of PC geometries. Indeed, some basic PC design parameters, such as the cells lattice d 0 , the substrate height h, the Phoenix cycle parameters (which define the cells smooth geometrical variation versus their reflection properties [1], [2]) can be explored in a preliminary design phase very efficiently [3]. The preliminary design phase is intended to help the designer explore and choose some relevant parameters before a more accurate full-wave analysis, avoiding several computationally expensive EM simulations.…”

Section: Final Pc Modelmentioning

confidence: 99%

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A General Equivalent Circuit Model for Phoenix Cells

Guarriello

Courtin

Loison

et al. 2021

IEEE Trans. Antennas Propagat.

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Final Pc Modelmentioning

confidence: 99%

“…Thanks to its rebirth property, it has the unique capability to come back to its initial geometry after a complete 360 • cycle, thus naturally preventing any abrupt variation in the design of quasi-periodic structures, for instance. In [2] first and second-order PCs are mapped on a sphere surface, highlighting other good PC features.…”

Section: Introductionmentioning

confidence: 99%

A General Equivalent Circuit Model for Phoenix Cells

Guarriello

Courtin

Loison

et al. 2021

IEEE Trans. Antennas Propagat.

Self Cite

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“…The design of reflectarrays composed of second‐order phoenix cells was presented in Reference 166. Fast characterization of these cells was made possible by using ANNs, allowing to obtain a spherical mapping that complies with the results obtained by full‐wave simulations with the local periodicity assumption.…”

Section: Predicting Antenna Parameters With Machine Learning Modelsmentioning

confidence: 99%

A review on the design and optimization of antennas using machine learning algorithms and techniques

Misilmani

Naous

Khatib

2020

Int J RF Microw Comput Aided Eng

102

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This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

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“…to take into account mutual coupling effects), in which case local optimization methods would normally be applied because of excessive costs of global routines (Bencivenni et al, 2016;Siragusa et al, 2012). This is especially the case when handling very large arrays, in particular reflectarrays, comprising up to thousands of elements (Zhou et al, 2015;Richard et al, 2019;Prado et al, 2019).…”

Section: Expedited Antenna Optimizationmentioning

confidence: 99%

Expedited antenna optimization with numerical derivatives and gradient change tracking

Pietrenko‐Dabrowska

Kozieł

2019

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Purpose The purpose of this study is to propose a framework for expedited antenna optimization with numerical derivatives involving gradient variation monitoring throughout the optimization run and demonstrate it using a benchmark set of real-world wideband antennas. A comprehensive analysis of the algorithm performance involving multiple starting points is provided. The optimization results are compared with a conventional trust-region (TR) procedure, as well as the state-of-the-art accelerated TR algorithms. Design/methodology/approach The proposed algorithm is a modification of the TR gradient-based algorithm with numerical derivatives in which a monitoring of changes of the system response gradients is performed throughout the algorithm run. The gradient variations between consecutive iterations are quantified by an appropriately developed metric. Upon detecting stable patterns for particular parameter sensitivities, the costly finite differentiation (FD)-based gradient updates are suppressed; hence, the overall number of full-wave electromagnetic (EM) simulations is significantly reduced. This leads to considerable computational savings without compromising the design quality. Findings Monitoring of the antenna response sensitivity variations during the optimization process enables to detect the parameters for which updating the gradient information is not necessary at every iteration. When incorporated into the TR gradient-search procedures, the approach permits reduction of the computational cost of the optimization process. The proposed technique is dedicated to expedite direct optimization of antenna structures, but it can also be applied to speed up surrogate-assisted tasks, especially solving sub-problems that involve performing numerous evaluations of coarse-discretization models. Research limitations/implications The introduced methodology opens up new possibilities for future developments of accelerated antenna optimization procedures. In particular, the presented routine can be combined with the previously reported techniques that involve replacing FD with the Broyden formula for directions that are satisfactorily well aligned with the most recent design relocation and/or performing FD in a sparse manner based on relative design relocation (with respect to the current search region) in consecutive algorithm iterations. Originality/value Benchmarking against a conventional TR procedure, as well as previously reported methods, confirms improved efficiency and reliability of the proposed approach. The applications of the framework include direct EM-driven design closure, along with surrogate-based optimization within variable-fidelity surrogate-assisted procedures. To the best of the authors’ knowledge, no comparable approach to antenna optimization has been reported elsewhere. Particularly, it surmounts established methodology by carrying out constant supervision of the antenna response gradient throughout successive algorithm iterations and using gathered observations to properly guide the optimization routine.

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Spherical Mapping of the Second-Order Phoenix Cell for Unbounded Direct Reflectarray Copolar Optimization

Cited by 18 publications

References 30 publications

A General Equivalent Circuit Model for Phoenix Cells

A General Equivalent Circuit Model for Phoenix Cells

A review on the design and optimization of antennas using machine learning algorithms and techniques

Expedited antenna optimization with numerical derivatives and gradient change tracking

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