Design Optimization Of Antennas Research Articles

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.

Curved-Pentagonal Planar Monopole Antenna for UHF Television Broadcast Receiving Antenna

A planar monopole antenna is an aerial, which the radiating element is perpendicular to the ground plane. It has five equal curvy sides and works at Ultra High Frequency (UHF) band of terrestrial broadcast 478–806 MHz. The curvy sides are made of five equal trimmed ellipses and separated 72o each ellipse to another, form pentagonal shape. Optimizations are obviously necessary to gain the antenna performance at the desired frequency range. The dimensions to optimize this antenna performance are the length of the sides, the offset of curvature, the antenna height from the ground plane, and the ground plane size. Optimization process is done by simulating the proposed antenna with calculated designs using Computer Simulation Technology (CST) Studio Suite 2015 software. The optimized antenna design then fabricated with a 75 W coaxial line fed, measured, and results: Voltage Standing Wave Ratio (VSWR) range of 1.05–1.28, antenna gain at 600 MHz is 15.33 dBi, elliptical polarized, and omnidirectional. With these features, this antenna should satisfy the requirements of UHF television broadcasting.

Comparison of evolutionary algorithms for LPDA antenna optimization

AbstractA novel approach to broadband log‐periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log‐periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front‐to‐rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.

Antenna Design and Direction of Arrival Estimation in Meta-Heuristic Paradigm

Antennas are considered as a significant component in any wireless system. There are numerous factors and constraints that affect its design. Therefore, recently several algorithms are developed to allow the designers optimize the antenna with respect to numerous different criteria, general constraints and the desired performance characteristics. In recent years there has been an increasing attention to some novel evolutionary techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bacteria-Foraging (BF), Biogeography Based Optimization (BBO), and Differential Evolution (DE) that used for antenna optimization. The current study discussed three popular population-based meta-heuristic algorithms for optimal antenna design and direction of arrival estimation. Basically, single and multi-objective population-based meta-heuristic algorithms are included. Besides hybrid methods are highlighted. This paper reviews antenna array design optimization as well as direction of arrival optimization problem for different antennas configurations.

Simulation-driven design of compact ultra-wideband antenna structures

Purpose – The purpose of this paper is to investigate strategies and algorithms for expedited design optimization and explicit size reduction of compact ultra-wideband (UWB) antennas. Design/methodology/approach – Formulation of the compact antenna design problem aiming at explicit size reduction while maintaining acceptable electrical performance is presented. Algorithmic frameworks are described suitable for handling various design situations and involving simulation models without and with response gradients available. Numerical and experimental case studies are provided demonstrating feasibility of solving real-world miniaturized antenna design tasks. Findings – It is possible, through appropriate combination of the global and local optimization methods, surrogate modeling techniques and response correction methods, to find optimum dimensions of antenna structures that minimize antenna size while maintaining acceptable electrical performance. Design optimization can be performed at practically feasible computational costs. Research limitations/implications – The study summarizes recent advances in miniaturization-oriented design optimization of UWB antennas. The presented techniques reach far beyond the commonly used design approaches based on parameter sweeps and similar hands-on methods, particularly in terms of automation, reliability, and reduction of the computational costs of the design processes. Originality/value – The proposed design problem formulation and algorithmic frameworks proved useful for rapid design of compact UWB antenna structures, which is extremely challenging when using conventional methods. To the knowledge, this is the first attempt to efficient solving of this type of design problems, especially in the context of explicit antenna size reduction.

Low-cost multi-objective optimization and experimental validation of UWB MIMO antenna

Purpose – The purpose of this paper is to validate methodologies for expedited multi-objective design optimization of complex antenna structures both numerically and experimentally. Design/methodology/approach – The task of identifying the best possible trade-offs between the antenna size and its electrical performance is formulated as multi-objective optimization problem. Algorithmic frameworks are described for finding Pareto-optimal designs using auxiliary surrogate models and two alternative approaches to design refinement: response correction techniques and co-kriging. Numerical and experimental case studies are provided to demonstrate feasibility of solving real-world and complex antenna design tasks. Findings – It is possible, through appropriate combination of the surrogate modeling techniques (both data driven and physics based) and response correction methods, to find the set of alternative designs representing the best possible trade-offs between conflicting design objectives, here, electrical performance and size. Design optimization can be performed at practically feasible computational costs. Research limitations/implications – The study demonstrates feasibility of automated multi-objective design optimization of antennas at low computational cost. The presented techniques reach beyond the commonly used design approaches based on parameter sweeps and similar hands-on methods, particularly in terms of automation, reliability and reduction of the computational costs of the design processes. Originality/value – Multi-objective design of antenna structures is very challenging when high-fidelity electromagnetic simulations are utilized for performance evaluation of the structure at hand. The proposed design framework permits rapid optimization of complex structures (here, MIMO antenna), which is hardly possible using conventional methods.

Spaceborne Antenna RF Radiation Protection Methods

Applications of the method in optimizing arrangement of antennas in small satellite platforms are investigated. Satellite antenna layout EMC simulation modeling, analysis and design optimization has been described in the article. Taking a LEO satellite prototype, we have established a satellite simulation model simulation model and the respective antennas, and calculate the degree of coupling between the antennas, and finally optimize the design of poor antenna placement. The pre-layout of the antenna system is arranged according to the engineering requirements and the design principle for electromagnetic compatibility (EMC) of antennas. After modeling optimization, several more serious mutual interference significantly improved antenna isolation. Simulation results show that, after a spaceborne antenna optimized layout, the original mutual interference serious antenna isolation performance improves. Spaceborne Antenna compatibility analysis simulation model to accurately establish can help RF radiation protection solutions to meet the design expectations.

Cost-Efficient Design Optimization of Compact Patch Antennas With Improved Bandwidth

In this letter, a surrogate-assisted optimization procedure for fast design of compact patch antennas with enhanced bandwidth is presented. The procedure aims at addressing a fundamental challenge of the design of antenna structures with complex topologies, which is simultaneous adjustment of numerous geometry parameters. The latter is necessary in order to find a truly optimum design and cannot be executed—at the level of high-fidelity electromagnetic (EM)-simulation models—using conventional numerical optimization procedures due to prohibitive computational cost. Here, we employ coarse-discretization EM simulations as faster representation of the antenna under design (referred to as a low-fidelity model). The low-fidelity model is enhanced using a combination of frequency scaling and response correction techniques. Frequency scaling is particularly suitable for reducing misalignment between EM models of various fidelities in case of narrowband structures. Iterative correction-prediction loop is capable to identify an optimum design at a reasonably low computational cost while adjusting all relevant geometry parameters at the same time. For the sake of illustration, we consider a modified patch antenna based on transversal signal-interference feed and additional slots in the radiator. Upon optimization, the antenna provided 29% bandwidth while maintaining a compact footprint of 645 $~\hbox{mm}^2$ . Experimental validation is also provided.

Design and Test of an Improved Dipole Antenna for Detecting Enclosure Structure Defects by Cross-Hole GPR

In underground engineering, the enclosure structure is a crucial part to guarantee the safety of excavation in soft ground regions. To inspect defects in the enclosure structure and assess its integrity, the cross-hole ground-penetrating radar (GPR) is considered as a promising tool. To make this technique feasible for underground structure applications, suitable antennas should be devised. In this paper, we designed an improved dipole antenna for a stepped frequency cross-hole GPR system to detect enclosure structure defects. Numerical simulations were adopted to optimize antenna design parameters including the half-cone angle θ, the feeding gap g, the cylinder radius r, and the cylinder length l. Then the optimized antenna was fabricated, and the performance of the antenna was evaluated by measuring the return loss, the voltage standing wave ratio (VSWR), the phase response, and the coupling. The results indicated that the proposed antenna operated well in the frequency range from 0.45 to 1.1 GHz and had the best performance at 0.52 GHz. Moreover, a physical model experiment was carried out and proved the antenna's feasibility for enclosure structure defects detection.

Influence of impairments due to dispersive propagation on the antenna design for body-based applications

In this paper, we analyze the frequency-dependent feature of the human body as radio propagation channel and the influence of that characteristic on the design of antennas for body-based applications. We describe the main impairments due to the frequency dispersion propagation through the body channel. Firstly, we describe the formation of the electromagnetic fields called Brillouin precursors which are responsible for another vital impairment: broadening of the time width of a transmitted signal. Later, we show a theoretical radio channel characterization of a human tissue that is affected by the frequency dispersion. Following, we describe three solutions to the described problematic optimal design of waveforms matched to the body channel, anti-dispersive filtering, and optimal antenna design. We introduce two broadband antennas offering a flat frequency response, minimizing the formation of precursors that ensures optimal time domain performance for ultrawideband body-based applications. Finally, we discuss the relation between the precursor formation and the parameters adopted to quantify the electromagnetic absorption inside biological tissues in order to review its definition under the dispersive perspective.

Research on radio coverage generated by antennas in confined space

Efficient radio wave coverage in confined space is one of the foundation factors to guarantee quality and reliability of communication which is important for the layout and optimization of the antenna system. However,the existing researches rarely consider the effect of the antenna parameters on field coverage in confined space. The research status and methods of radio wave coverage in confined space,aimed at railway tunnel,were reviewed. Then the deterministic models for field coverage prediction in the railway tunnel were adopted which were ray tracing method,modal method and parabolic equation method. By combining these methods,the effect of the antenna beam width,beam direction and fixed position in the cross section of the tunnel were analyzed. Then the field coverage characteristics in different regions were analyzed. At last,a three slopes propagation model was proposed for studying the field coverage characteristics in confined space.The method and model can provide the theoretical basis and a reference for antenna design and layout optimization in the tunnels.

Space mapping optimization of handset antennas considering EM effects of mobile phone components and human body

In order to meet antenna design specifications under realistic conditions, electromagnetic coupling effects between the antenna and its environment must be considered. In this work, an efficient antenna design optimization methodology that considers the influence of the human head and main mobile handset components on the antenna performance is presented. The computational optimization time is dramatically reduced by exploiting a Broyden-based input space mapping (SM) algorithm. Both coarse and fine models required for the SM algorithm are based on the finite-element method (FEM) and are implemented in the same simulator; simplifying the modeling process. However, our coarse model does not consider any object of the actual operational environment. In spite of that and other simplifications applied to the coarse model, the proposed optimization scheme is able to find a solution that meets the specifications in a realistic environment by performing an extremely small number of expensive fine model simulations. Our practical illustration opens up the feasibility of using this CAD methodology to optimize other RF devices that operate in close proximity to objects that affect its desired response, as it is the case for many wearable devices.

Efficient knowledge-based optimization of expensive computational models using adaptive response correction

Computer simulation has become an indispensable tool in engineering design as they allow an accurate evaluation of the system performance. This is critical in order to carry out the design process in a reliable manner without costly prototyping and physical measurements. However, high-fidelity computer simulations are computationally expensive. This turns to be a fundamental bottleneck when it comes to design automation using numerical optimization techniques. In particular, direct optimization of simulation models, typically, requires a large number of model evaluations, which may be impractical or even infeasible in a reasonable timeframe. Possibly the most promising approach to alleviate this difficulty is surrogate-based optimization (SBO), where direct optimization of expensive models is replaced by an iterative enhancement and re-optimization of fast surrogate models. While a large variety of surrogate modeling and optimization are available, the methods exploiting the so-called physics-based surrogates seem to be the most efficient ones because the knowledge about the system of interest embedded in the underlying (often simulation-based) low-fidelity model ensures good generalization of the surrogate and a rapid convergence of the SBO algorithm. In this paper, we review a specific technique of this class, that is, the adaptive response correction (ARC). We discuss the formulation of the method, its limitations and generalizations, as well as illustrate its application for solving problems in various areas, including microwave engineering, antenna design, and aerodynamic shape optimization.

Application of Teaching Learning Based Optimization in antenna designing

Numerous optimization techniques are studied and applied on antenna designs to optimize various performance parameters. Authors used many Multiple Attributes Decision Making (MADM) methods, which include, Weighted Sum Method (WSM), Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Analytic Hierarchy Process (AHP), ELECTRE, etc. Of these many MADM methods, TOPSIS and AHP are more widely used decision making methods. Both TOPSIS and AHP are logical decision making approaches and deal with the problem of choosing an alternative from a set of alternatives which are characterized in terms of some attributes. Analytic Hierarchy Process (AHP) is explained in detail and compared with WSM and WPM. Authors fi- nally used Teaching-Learning-Based Optimization (TLBO) technique; which is a novel method for constrained antenna design optimization problems.

Reconfigurable Antennas: Design and Applications

The advancement in wireless communications requires the integration of multiple radios into a single platform to maximize connectivity. In this paper, the design process of reconfigurable antennas is discussed. Reconfigurable antennas are proposed to cover different wireless services that operate over a wide frequency range. They show significant promise in addressing new system requirements. They exhibit the ability to modify their geometries and behavior to adapt to changes in surrounding conditions. Reconfigurable antennas can deliver the same throughput as a multiantenna system. They use dynamically variable and adaptable single-antenna geometry without increasing the real estate required to accommodate multiple antennas. The optimization of reconfigurable antenna design and operation by removing unnecessary redundant switches to alleviate biasing issues and improve the system's performance is discussed. Controlling the antenna reconfiguration by software, using Field Programmable Gate Arrays (FPGAs) or microcontrollers is introduced herein. The use of Neural Networks and its integration with graph models on programmable platforms and its effect on the operation of reconfigurable antennas is presented. Finally, the applications of reconfigurable antennas for cognitive radio, Multiple Input Multiple Output (MIMO) channels, and space applications are highlighted.

Multi-Objective Design Optimization of Planar Yagi-Uda Antenna Using Physics-Based Surrogates and Rotational Design Space Reduction

A procedure for low-cost multi-objective design optimization of antenna structures is discussed. The major stages of the optimization process include: (i) an initial reduction of the search space aimed at identifying its relevant subset containing the Pareto-optimal design space, (ii) constructionusing sampled coarse-discretization electromagnetic (EM) simulation dataof the response surface approximation surrogate, (iii) surrogate optimization using a multi-objective evolutionary algorithm, and (iv) the Pareto front refinement. Our optimization procedure is demonstrated through the design of a planar quasi Yagi-Uda antenna. The final set of designs representing the best available trade-offs between conflicting objectives is obtained at a computational cost corresponding to about 172 evaluations of the high-fidelity EM antenna model.