Dual-Band Slot Dipole Antenna Fed by a Coplanar Waveguide

Chen, You-Chieh; Chen, Shih‐Yuan; Hsu, Powen

doi:10.1109/aps.2006.1711396

Cited by 42 publications

(5 citation statements)

References 10 publications

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“…Numerical verification of the relevance of feature-based re-formulation described in Section 2 is executed using the following microstrip antenna structures: (i) Antenna I: a dual-band uniplanar dipole antenna [103] presented in Figure 6a, (ii) Antenna II: a triple-band uniplanar dipole antenna [93] shown in Figure 6b, along with (iii) Antenna III: a triple band U-slotted patch featuring L-slot defected ground structure (DGS) [104] presented in Figure 6c. Note that Antennas I and III have been already considered as illustration examples in Section 2; however, they are shown again in Figure 6 to make this section self-contained.…”

Section: Verification Antennasmentioning

confidence: 99%

On Accelerated Metaheuristic-Based Electromagnetic-Driven Design Optimization of Antenna Structures Using Response Features

Koziel,

Pietrenko-Dabrowska,

Pankiewicz

2024

Electronics

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Development of present-day antenna systems is an intricate and multi-step process requiring, among others, meticulous tuning of designable (mainly geometry) parameters. Concerning the latter, the most reliable approach is rigorous numerical optimization, which tends to be resource-intensive in terms of computing due to involving full-wave electromagnetic (EM) simulations. The cost-related issues are particularly pronounced whenever global optimization is necessary, typically carried out using nature-inspired algorithms. Although capable of escaping from local optima, population-based algorithms exhibit poor computational efficiency, to the extent of being hardly feasible when directly handling EM simulation models. A popular mitigation approach involves surrogate modeling techniques, facilitating the search process by replacing costly EM analyses with a fast metamodel. Yet, surrogate-assisted procedures feature complex implementations, and their range of applicability is limited in terms of design space dimensionality that can be efficiently handled. Rendering reliable surrogates is additionally encumbered by highly nonlinear antenna characteristics. This paper investigates potential benefits of employing problem-relevant knowledge in the form of response features into nature-inspired antenna optimization. As demonstrated in the recent literature, re-formulating the design task with the use of appropriately selected characteristic locations of the antenna responses permits flattening the functional landscape of the objective function, leading to faster convergence of optimization procedures. Here, we apply this concept to nature-inspired global optimization of multi-band antenna structures, and demonstrate its relevance, both in terms of accelerating the search process but also improving its reliability. The advantages of feature-based nature-inspired optimization are corroborated through comprehensive (based on three antenna structures) comparisons with a population-based search involving conventional (e.g., minimax) design problem formulation.

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Section: Verification Antennasmentioning

confidence: 99%

On Accelerated Metaheuristic-Based Electromagnetic-Driven Design Optimization of Antenna Structures Using Response Features

Koziel,

Pietrenko-Dabrowska,

Pankiewicz

2024

Electronics

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“…The second verification case is a triple-band uniplanar dipole antenna shown in Figure 4b. The structure is based on the design of [41], implemented on RO4350 substrate, and fed through a 50-ohm coplanar waveguide (CPW). The experimental setup is the same as for the first test case.…”

Section: Example Ii: Triple-band Uniplanar Dipole Antennamentioning

confidence: 99%

“…Figure 4. Verification case studies: (a) ultra-wideband monopole[39] (ground plane shown using light-shade gray), and (b) triple-band uniplanar dipole[41].…”

mentioning

confidence: 99%

Expedited Globalized Antenna Optimization by Principal Components and Variable-Fidelity EM Simulations: Application to Microstrip Antenna Design

2020

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Parameter optimization, also referred to as design closure, is imperative in the development of modern antennas. Theoretical considerations along with rough dimension adjustment through supervised parameter sweeping can only yield initial designs that need to be further tuned to boost the antenna performance. The major challenges include handling of multi-dimensional parameter spaces while accounting for several objectives and constraints. Due to complexity of modern antenna topologies, parameter interactions are often involved, leading to multiple local optima as well as difficulties in identifying decent initial designs that can be improved using local procedures. In such cases, global search is required, which is an expensive endeavor, especially if full-wave electromagnetic (EM) analysis is employed for antenna evaluation. This paper proposes a novel technique accommodating the search space exploration using local kriging surrogates and local improvement by means of trust-region gradient search. Computational efficiency of the process is achieved by constructing the metamodels over appropriately defined affine subspaces and incorporation of coarse-mesh EM simulations at the exploratory stages of the optimization process. The resulting framework enables nearly global search capabilities at the costs comparable to conventional gradient-based local optimization. This is demonstrated using two antenna examples and comparative studies involving multiple-start local tuning.

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“…• A dual-band uniplanar dipole antenna (Antenna I) shown in Fig. 3(a) [71]. The antenna is implemented on RO4350 substrate (ε r = 3.5, h = 0.76 mm).…”

Section: A Case Studiesmentioning

confidence: 99%

“…The objective is to construct the surrogate model valid for the following ranges of operating frequencies 2.0 GHz ≤ f 1 ≤ 3.0 GHz (lower band), and 4.0 GHz ≤ f 2 ≤ 5.5 GHz (upper band). The details about the reference designs and the parameter space can be found in [71]. In this case, the surrogate model is to be constructed for the objective space parameterized by the operating frequencies f 1 and f 2 = Kf 1 for 2.0 GHz ≤ f 1 ≤ 3.5 GHz, and 1.2 ≤ K ≤ 1.…”

Section: A Case Studiesmentioning

confidence: 99%

On Inadequacy of Sequential Design of Experiments for Performance-Driven Surrogate Modeling of Antenna Input Characteristics

Pietrenko‐Dabrowska

Koziel

2020

IEEE Access

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Design of contemporary antennas necessarily involves electromagnetic (EM) simulation tools. Their employment is imperative to ensure evaluation reliability but also to carry out the design process itself, especially, the adjustment of antenna dimensions. For the latter, traditionally used parameter sweeping is more and more often replaced by rigorous numerical optimization, which entails considerable computational expenses, sometimes prohibitive. A potentially attractive way of expediting the simulation-based design procedures is the replacement of expensive EM analysis by fast surrogate models (or metamodels). Unfortunately, due to the curse of dimensionality and considerable nonlinearity of antenna characteristics, applicability of conventional modeling methods is limited to structures described by small numbers of parameters within narrow ranges thereof. A recently proposed nested kriging technique works around these issues by allocating the surrogate model domain within the regions containing designs that are of high quality with respect to the selected performance figures. This paper investigates whether sequential design of experiments (DoE) is capable of enhancing the modeling accuracy over one-shot space-filling data sampling originally implemented in the nested kriging framework. Numerical verification carried out for two microstrip antennas indicates that no noticeable benefits can be achieved, which contradicts the commonsense expectations. This result can be explained by a particular geometry of the confined domain of the performance-driven surrogate. As this set consists of nearly-optimum designs, the average nonlinearity of the antenna responses therein is almost location independent, therefore optimum training data allocation should be close to uniform. This is indeed corroborated by our experiments.

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Dual-Band Slot Dipole Antenna Fed by a Coplanar Waveguide

Cited by 42 publications

References 10 publications

On Accelerated Metaheuristic-Based Electromagnetic-Driven Design Optimization of Antenna Structures Using Response Features

On Accelerated Metaheuristic-Based Electromagnetic-Driven Design Optimization of Antenna Structures Using Response Features

Expedited Globalized Antenna Optimization by Principal Components and Variable-Fidelity EM Simulations: Application to Microstrip Antenna Design

On Inadequacy of Sequential Design of Experiments for Performance-Driven Surrogate Modeling of Antenna Input Characteristics

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