Design Optimization Of Antennas Research Articles

Multiband, frequency reconfigurable, and metamaterial antennas design techniques: Present and future research directions

In recent years, the demand for multifunctional components has increased tremendously. Advancement in integrated technology has provided researchers with ample capability to fit diversified applications operating at different frequencies in one wireless device. Integration of multiband antennas in conjunction with frequency reconfigurability offers an optimal antenna design. In addition, the incorporation of metamaterials yields controllable and promising radiation properties. This paper contributes uniquely to the field of antenna design in two ways. First, it provides the domain and technical aspects associated with multiband, frequency reconfigurable, and metamaterial antennas. Second, it provides a critical review of literature. The paper additionally highlights the deficiencies in the design techniques reported in the literature.

A Slotted Lotus Shaped Microstrip Antenna based an EBG Structure

The objective of this paper is to study intensively the design of a printed slotted patch based lotus shape structure mounted on a dielectric substrate backed with an electromagnetic band Gap (EBG) layer for wideband applications. The dielectric substrate is made of a Roger RT/duroid®5880 layer. An EBG layer is introduced on the back profile of the substrate to provide a high gain bandwidth product over wide frequency bands. The antenna is fed with a novel coplanar waveguide (CPW) structure of a flared geometry; therefore, the ground plane is mounted on the same substrate surface with the patch structure. A conductive trace is introduced at the substrate back from the bottom connected to the CPW through two shoring plates to remove the effects of the EBG layer on the feed structure. The EBG performance and the antenna design methodology are discussed using analytical analyses and numerical parametric studies, respectively. The numerical simulation is conducted using CST MWS Finally; the optimal antenna design is fabricated and measured for validation to be compared to the simulated results.

Reduced-cost surrogate modeling of input characteristics and design optimization of dual-band antennas using response features

In this article, a procedure for low-cost surrogate modeling of input characteristics of dual-band antennas has been discussed. The number of training data required for construction of an accurate model has been reduced by representing the antenna reflection response to the level of suitably defined feature points. The points are allocated to capture the critical features of the reflection characteristic, such as the frequencies and the levels of the resonances, and supplemented by the additions (infill) points, which is necessary to provide sufficient data that allows restoring the entire response through interpolation. Because the coordinates of the feature points exhibit less nonlinear behavior (as a function of antenna geometry parameters) compared to S-parameters as a function of frequency, surrogate model construction can be realized with a smaller number of data points. The presented modeling approach is demonstrated using an example of a planar dipole antenna. Also, the feature-based method is favorably compared to direct modeling of reflection characteristics using kriging. The relevance of the technique is further verified by its application for design optimization.

Reliable low‐cost surrogate modeling and design optimisation of antennas using implicit space mapping with substrate segmentation

In this work, a reliable methodology for fast simulation-driven design optimisation of antenna structures is proposed. The authors' approach exploits implicit space mapping (ISM) technology. To adopt it for handling antenna structures, they introduce substrate segmentation with separate dielectric permittivity value assigned for each segment as ISM preassigned parameters. At the same time, the coarse model for space mapping is established by reducing discretisation level of the EM analysis of the antenna structure at hand. Further, the proposed approach is generalised to permit execution of ISM in case of dielectric resonator antennas (DRAs), which is realised by segmentation of the DR and appropriate assignment of permittivity values. The discussed methodology is demonstrated through optimisation of a bandwidth-enhanced patch antenna and a triangular DRA. Comparison with state-of-the-art surrogate-assisted design methods, here, frequency and output space mapping, as well as conventional optimisation using pattern search, is also provided.

A novel parameterization method for the topology optimization of metallic antenna design

In this paper, based on a tangential interpolation function and an adaptively increasing penalty-factor strategy (TIPS), a novel parameterization method with a self-penalization scheme aimed for the topology optimization of metallic antenna design is proposed. The topology description is based on the material distribution approach. The proposed tangential interpolation function aims to associate the material resistance with design variables, in which the material resistance is expressed in the arctangent scale and the arctangent resistance is interpolated with the design variables using the rational approximation of material properties. During the optimization process, a strategy with an adaptively increasing penalty factor is used to eliminate the remaining gray scale elements, as illustrated in examples, in the topology optimization based on the proposed tangential interpolation function. Design results of typical examples express the effectiveness of the proposed TIPS parameterization.

Frequency agile quadrilateral patch and slot based optimal antenna design for cognitive radio system

This article presents a compact quadrilateral patch antenna design for cognitive radio system having dimensions of 50 × 50 × 1.6 mm3. The proposed antenna is designed with a triangular shape slot structure and quadrilateral patch with two step feedline on the either side of the FR4 substrate which is capable of switching for twelve different bands of frequency. The frequency range varies from 2.46 to 3.90 GHz, with a relative impedance bandwidth of around 45%. To achieve frequency agility six PIN diodes are used to vary the length of higher inclined arms of the slot. In the proposed approach, incorporation of biasing circuit into the ground plane reduces the parasitic effect, which improves the performance of the antenna. Moreover, the robustness of the proposed design is demonstrated in terms of reduction of antenna size, a number of frequency state, frequency ratio and number of PIN diodes used for frequency agility. The simulated and measured results for reflection coefficient, radiation pattern, relative bandwidth, gain, and efficiency are found to be in good agreement with each other.

Responsivity Enhancement of Metal-Insulator-Semiconductor Photodetectors on Silicon-on-Insulator Substrates by Plasmonic Nanoantennas

We report metal-insulator-semiconductor photodetectors fabricated on a silicon-on-insulator substrate, exhibiting an enhanced responsivity in the near-infrared due to the incorporation of Au-nanoantennas at the optical input port. The optimal antenna design and the photoresponse enhancements have been calculated using detailed finite-difference time-domain simulations. These nanoantennas modify the propagation of the input optical signals. In this regime, strong plasmonic local field enhancements are observed. The maximum responsivity enhancement, 40%, was obtained at 780 nm; while at wavelengths shorter than 720 nm, the responsivity is reduced due to absorption losses in the Au-nanoantennas.

Design Optimization and Trade-Offs of Miniaturized Wideband Antenna for Internet of Things Applications

Abstract Internet of Things (IoT) will play an important role in modern communication systems. Thousands of devices will talk to each other at the same time. Clearly, smart and efficient hardware will play a vital role in the development of IoT. In this context, the importance of antennas increases due to them being essential parts of communication networks. For IoT applications, a small size with good matching and over a wide frequency range is preferred to ensure reduced size of communication devices. In this paper, we propose a structure and discuss design optimization of a wideband antenna for IoT applications. The antenna consists of a stepped-impedance feed line, a rectangular radiator and a ground plane. The objective is to minimize the antenna footprint by simultaneously adjusting all geometry parameters and to maintain the electrical characteristic of antenna at an acceptable level. The obtained design exhibits dimensions of only 3.7 mm × 11.8 mm and a footprint of 44 mm2, an omnidirectional radiation pattern, and an excellent pattern stability. The proposed antenna can be easily handled within compact communication devices. The simulation results are validated through measurements of the fabricated antenna prototype.

Design optimization of CPW-fed microstrip patch antenna using constrained ABFO algorithm

This paper explores the potential of bio-inspired soft computational technique known as adaptive bacterial foraging optimization (ABFO) for the joint optimization of geometrical parameters of compact coplanar waveguide (CPW)-fed microstrip patch antenna with defected ground structure. The presented research work is divided into three phases. In the initial phase, the intended antenna is designed and analyzed using finite element-based electromagnetic simulator Ansoft HFSS 15.0. In the subsequent phase, the analytical equations of various design parameters are modeled using curve fitting technique in MATLAB and root mean square error-based fitness functions are derived for individual design parameters. Then, a joint cost function is formulated from individual fitness functions for evaluation in optimization algorithm. Adaptive BFO is an improvement in classical BFO algorithm that dynamically adjusts the run-length unit parameter to maintain the balance between exploration–exploitation trade-off. In the final phase, a variation in the adaptive BFO algorithm termed as ‘constrained ABFO’ is projected and designed to suit the bounded constraints imposed by anticipated antenna structure. The modified algorithm is efficaciously used for joint optimization of specific design parameters to transform ‘dual-band performance’ into ‘broadband performance’ for high-speed point-to-point wireless services. The performance of design optimization using constrained ABFO is compared with the original BFO, particle swarm optimization (PSO), hybrid bacterial foraging–particle swarm optimization (BF-PSO), invasive weed optimization (IWO) and artificial bee colony (ABC) techniques to scrutinize its adequacy.

Optimization of patch antennas via multithreaded simulated annealing based design exploration

Abstract In this paper, we present a new software framework for the optimization of the design of microstrip patch antennas. The proposed simulation and optimization framework implements a simulated annealing algorithm to perform design space exploration in order to identify the optimal patch antenna design. During each iteration of the optimization loop, we employ the popular MEEP simulation tool to evaluate explored design solutions. To speed up the design space exploration, the software framework is developed to run multiple MEEP simulations concurrently. This is achieved using multithreading to implement a manager-workers execution strategy. The number of worker threads is the same as the number of cores of the computer that is utilized. Thus, the computational runtime of the proposed software framework enables effective design space exploration. Simulations demonstrate the effectiveness of the proposed software framework. Highlights A software framework for the optimization of the design of microstrip patch antennas. A simulated annealing algorithm to perform the design space exploration. The popular MEEP simulator is employed to evaluate explored solutions for accuracy. Multithreading is used as a technique to speed-up the proposed tool.

Rapid Design Optimization of Multi-Band Antennas by Means of Response Features

AbstractThis work examines the reduced-cost design optimization of dual- and multi-band antennas. The primary challenge is independent yet simultaneous control of the antenna responses at two or more frequency bands. In order to handle this task, a feature-based optimization approach is adopted where the design objectives are formulated on the basis of the coordinates of so-called characteristic points (or response features) of the antenna response. Due to only slightly nonlinear dependence of the feature points on antenna geometry parameters, optimization can be attained at a low computational cost. Our approach is demonstrated using two antenna structures with the optimum designs obtained in just a few dozen of EM simulations of the respective structure.

Design of a Ceramic Patch Antenna for Artillery Projectile Positioning Applications

본 논문에서는 GPS/GLONASS 신호 수신을 위해 포탄 신관에 탑재되는 소형 원편파 패치 안테나를 제안하였다. 제안된 안테나는 모서리가 절단된 슬롯형 세라믹 패치와 작은 원형 접지판으로 구성된다. 충분한 원편파 대역폭을 얻기 위해 패치 중앙의 2곳에 소형 슬롯을 배치하고 안테나의 요구되는 임피던스 대역폭을 얻기 위해 패치의 상부 왼쪽 모서리를 절단하였다. 신관 내부에 설계된 안테나는 높이 6㎜와 직경 36㎜의 공간을 차지한다. 안테나의 최적 설계 및 주요 설계 변수에 따른 영향은 CST사의 Microwave StudioTM 프로그램으로 분석하였다. 제작된 안테나는 플라스틱 폼으로 포팅되어 있는 신관 레이돔 내부에 실장된다. 제작된 안테나는 2.5~4.2㏈ic 이득, 5.1~11.6㏈ 축비 및 1.57~1.62㎓에서 –10㏈이하의 반사계수 특성을 보였다.

Constrained multi‐objective antenna design optimization using surrogates

AbstractA novel surrogate‐based constrained multi‐objective optimization algorithm for simulation‐driven optimization is proposed. The evolutionary algorithms usually applied in antenna design optimization typically require a large number of objective function evaluations to converge. The efficient constrained multiobjective optimization algorithm described in this paper identifies Pareto‐optimal solutions satisfying the required constraints using very few function evaluations. This leads to substantial savings in time and drastically reduces the time to market for expensive antenna design optimization problems. The efficiency of the approach is demonstrated on the design of an L1‐band GPS antenna. The algorithm automatically optimizes the antenna geometry, parametrized by 5 design variables with performance constraints on three objectives. The results are compared with well‐established multiobjective optimization evolutionary algorithms.

Antenna current optimization for optimal antenna design in different frequency bands using VSO-NM algorithm

In this article, the antenna partial gain to -factor based on the antenna current optimization is used as figure of merit for antenna design proposing an efficient global hybrid technique that combining very simple optimization (VSO) and Nelder-Mead (NM) algorithm. To validate the strength of the approach, a set of three antennas operating within different frequency bands of super high frequency, submillimeter, and light spectrum are addressed and optimized. The antennas are analyzed completely using finite difference time domain method. The results showed the strength of the approach of optimizing antenna gain to -factor based on antenna current optimization using the hybrid VSO-NM algorithm in different frequency bands. Compared to stand-alone VSO, the hybrid VSO-NM algorithm showed the ability to reduce the processing time on average by 58.73% in addition to enhancing the search capability by 43%.

Expedited simulation-driven design optimization of UWB antennas by means of response features

In this work, a method for fast design optimization of broadband antennas is considered. The approach is based on a feature-based optimization (FBO) concept where reflection characteristics of the structure at hand are formulated in terms of suitably defined feature points. Redefinition of the design problem allows for reducing the design optimization cost, because the dependence of feature point coordinates on antenna dimensions is less nonlinear than for the original frequency characteristics (here, S-parameters). This results in faster convergence of the optimization algorithm. The cost of the design process is further reduced using variable-fidelity electromagnetic (EM) simulation models. In case of UWB antennas, the feature points are defined, among others, as the levels of the reflection characteristic at its local in-band maxima, as well as location of the frequency point which corresponds to acceptable reflection around the lower corner frequency within the UWB band. Also, the number of characteristic points depends on antenna topology and its dimensions. Performance of FBO-based design optimization is demonstrated using two examples of planar UWB antennas. Moreover, the computational cost of the approach is compared with conventional optimization driven by a pattern search algorithm. Experimental validation of the numerical results is also provided.

Pareto Ranking Bisection Algorithm for EM-Driven Multi-Objective Design of Antennas in Highly-Dimensional Parameter Spaces

A deterministic technique for fast surrogate-assisted multi-objective design optimization of antennas in highly-dimensional parameters spaces has been discussed. In this two-stage approach, the initial approximation of the Pareto set representing the best compromise between conflicting objectives is obtained using a bisection algorithm which finds new Pareto-optimal designs by dividing the line segments interconnecting previously found optimal points, and executing poll-type search that involves Pareto ranking. The initial Pareto front is generated at the level of the coarsely-discretized EM model of the antenna. In the second stage of the algorithm, the high-fidelity Pareto designs are obtained through optimization of corrected local-approximation models. The considered optimization method is verified using a 17-variable uniplanar antenna operating in ultra-wideband frequency range. The method is compared to three state-of-the-art surrogate-assisted multi-objective optimization algorithms.

A Novel Structure and Design Optimization of Compact Spline-Parameterized UWB Slot Antenna

Abstract In this paper, a novel structure of a compact UWB slot antenna and its design optimization procedure has been presented. In order to achieve a sufficient number of degrees of freedom necessary to obtain a considerable size reduction rate, the slot is parameterized using spline curves. All antenna dimensions are simultaneously adjusted using numerical optimization procedures. The fundamental bottleneck here is a high cost of the electromagnetic (EM) simulation model of the structure that includes (for reliability) an SMA connector. Another problem is a large number of geometry parameters (nineteen). For the sake of computational efficiency, the optimization process is therefore performed using variable-fidelity EM simulations and surrogate-assisted algorithms. The optimization process is oriented towards explicit reduction of the antenna size and leads to a compact footprint of 199 mm2 as well as acceptable matching within the entire UWB band. The simulation results are validated using physical measurements of the fabricated antenna prototype.

SADEA-II: A generalized method for efficient global optimization of antenna design

Abstract Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Highlights An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations. A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).

Performance enhancement of multiband antennas through a two-stage optimization technique

This paper presents an optimization technique that aims at multiband antenna design. The proposed method is based on the framework of multi-objective evolutionary algorithms, and a two-stage mechanism that balances the degree of optimizing impedance matching and the degree of providing a wide impedance bandwidth is incorporated. Conventionally, the design optimization of multiband antennas relies on minimizing the maximum reflection coefficient or maximizing the area under the return-loss curve over targeted frequency bands. However, these widely used methods direct an optimization algorithm to improper solution sub-domains in the multiband design problem. To overcome the limitation of these conventional methods, the general rule of objective functions is thoroughly investigated in this paper. Furthermore, a two-stage optimizer is designed based on what the multiband optimization problem needs. With the use of the proposed method, two multiband antennas for mobile communication systems covering 824–960 MHz and 1710–2170 MHz are successfully developed. Simulated and measured results show that the proposed technique outperforms conventional optimization approaches significantly.

Rapid design optimization of antennas using variable-fidelity EM models and adjoint sensitivities

Purpose Development of techniques for expedited design optimization of complex and numerically expensive electromagnetic (EM) simulation models of antenna structures validated both numerically and experimentally. The paper aims to discuss these issues. Design/methodology/approach The optimization task is performed using a technique that combines gradient search with adjoint sensitivities, trust region framework, as well as EM simulation models with various levels of fidelity (coarse, medium and fine). Adaptive procedure for switching between the models of increasing accuracy in the course of the optimization process is implemented. Numerical and experimental case studies are provided to validate correctness of the design approach. Findings Appropriate combination of suitable design optimization algorithm embedded in a trust region framework, as well as model selection techniques, allows for considerable reduction of the antenna optimization cost compared to conventional methods. Research limitations/implications The study demonstrates feasibility of EM-simulation-driven design optimization of antennas at low computational cost. The presented techniques reach beyond the common design approaches based on direct optimization of EM models using conventional gradient-based or derivative-free methods, particularly in terms of reliability and reduction of the computational costs of the design processes. Originality/value Simulation-driven design optimization of contemporary antenna structures is very challenging when high-fidelity EM simulations are utilized for performance utilization of structure at hand. The proposed variable-fidelity optimization technique with adjoint sensitivity and trust regions permits rapid optimization of numerically demanding antenna designs (here, dielectric resonator antenna and compact monopole), which cannot be achieved when conventional methods are of use. The design cost of proposed strategy is up to 60 percent lower than direct optimization exploiting adjoint sensitivities. Experimental validation of the results is also provided.