Abstract: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… Show more
“…For the second example, consider the T-slot planar inverted F handset (PIFA) antenna proposed in [38]. Its geometry is shown in Fig.…”
Section: B Dual-band Planar Inverted F Handset Antenna With Slotted mentioning
confidence: 99%
“…The antenna design is intended to operate at the following bands: GSM900 (880-960 MHz), GSM1900 (1850-1990 MHz), UMTS2100 and WCDMA2100 (1920-2170 MHz). The structure is implemented in COMSOL by using the simulation bounding box dimensions, boundary conditions, and the meshing scheme proposed in [38]. All metals are defined as perfect electric conductors.…”
Section: B Dual-band Planar Inverted F Handset Antenna With Slotted mentioning
confidence: 99%
“…We can identify two types of surrogate models: physical and functional surrogates. A physical surrogate is usually implemented by a quasi-static approximation or an equivalent circuit; it can also be implemented in the same EM simulator used for the original structure under design, but using a coarse discretization [3] and removing some details of the original structure to speed up the simulation time [4]. However, these simplified and coarsely discretized full-wave EM models may exhibit numerical noise, discontinuous behavior, and nonnegligible simulation time with respect to the corresponding original fine model [5].…”
A general formulation to develop EM-based polynomial surrogate models in frequency domain utilizing the multinomial theorem is presented in this paper. Our approach is especially suitable when the number of learning samples is very limited and no physics-based coarse model is available. We compare our methodology against other four surrogate modeling techniques: response surface modeling, support vector machines, generalized regression neural networks, and Kriging. Results confirm that our modeling approach has the best performance among these techniques when using a very small amount of learning base points on relatively small modeling regions. We illustrate our technique by developing a surrogate model for an SIW interconnect with transitions to microstrip lines, a dual band T-slot PIFA handset antenna, and a high-speed package interconnect. Examples are simulated on a commercially available 3D FEM simulator.
“…For the second example, consider the T-slot planar inverted F handset (PIFA) antenna proposed in [38]. Its geometry is shown in Fig.…”
Section: B Dual-band Planar Inverted F Handset Antenna With Slotted mentioning
confidence: 99%
“…The antenna design is intended to operate at the following bands: GSM900 (880-960 MHz), GSM1900 (1850-1990 MHz), UMTS2100 and WCDMA2100 (1920-2170 MHz). The structure is implemented in COMSOL by using the simulation bounding box dimensions, boundary conditions, and the meshing scheme proposed in [38]. All metals are defined as perfect electric conductors.…”
Section: B Dual-band Planar Inverted F Handset Antenna With Slotted mentioning
confidence: 99%
“…We can identify two types of surrogate models: physical and functional surrogates. A physical surrogate is usually implemented by a quasi-static approximation or an equivalent circuit; it can also be implemented in the same EM simulator used for the original structure under design, but using a coarse discretization [3] and removing some details of the original structure to speed up the simulation time [4]. However, these simplified and coarsely discretized full-wave EM models may exhibit numerical noise, discontinuous behavior, and nonnegligible simulation time with respect to the corresponding original fine model [5].…”
A general formulation to develop EM-based polynomial surrogate models in frequency domain utilizing the multinomial theorem is presented in this paper. Our approach is especially suitable when the number of learning samples is very limited and no physics-based coarse model is available. We compare our methodology against other four surrogate modeling techniques: response surface modeling, support vector machines, generalized regression neural networks, and Kriging. Results confirm that our modeling approach has the best performance among these techniques when using a very small amount of learning base points on relatively small modeling regions. We illustrate our technique by developing a surrogate model for an SIW interconnect with transitions to microstrip lines, a dual band T-slot PIFA handset antenna, and a high-speed package interconnect. Examples are simulated on a commercially available 3D FEM simulator.
“…Several other antennas have also been proposed in Refs. for mobile phone applications. However, as full‐metal‐shell handsets are getting more and more popular nowadays due to their better hand‐feeling and fantastic appearance, such evolution also raises a big challenge in antenna design as the metal‐shell associated with surrounding electronic components like front‐back‐cameras and telephone receiver would affect the antenna performance.…”
A compact dual‐band planar inverted‐F antenna (PIFA) with U‐shaped strip is proposed in this work for all‐metal‐shell mobile telephone application. As metal‐shell handsets are getting more and more popular nowadays, it raises a big challenge in antenna design as the metal‐shell associated with surrounding electronic components like front‐back‐cameras and telephone receiver would affect the antenna performance. This work provides an optional solution to alleviate this problem, where the metal shell of the handset and a U‐shaped strip are utilized as part of the antenna. The proposed antenna is able to generate radiation at 2.4 GHz for Wi‐Fi application with the help of the metal shell while using the U‐shaped strip can achieve a resonance at 1.575 GHz for GPS application. A prototype has been fabricated to verify the radiation performance in a practical handset test environment.
“…is an approximation of fine model optimum. In the past decade, several advances in space mapping such as aggressive space mapping [5] - [7], implicit space mapping (ISM) [27][28], output space mapping (OSM) [29] - [31], space mapping interpolating surrogates (SMISs) [30], inverse space mapping [32] - [33], have proven successful for difficult optimization problems of microwave filters.…”
Optimization and modeling techniques are the essential part of design process of microwave filters. Space mapping is a recognized method for speeding up electromagnetic (EM) optimization, and has been applied to microwave filter design.In the first part of this thesis, a cognition-driven formulation of space mapping method is proposed and applied to EM-based filter optimization to increase optimization efficiency and the ability to avoid being trapped in local minima. This new technique utilizes two sets of intermediate feature space parameters, including feature frequency parameters and ripple height parameters. The design variables are mapped to the feature frequency parameters, which are further mapped to the ripple height parameters. By formulating the cognition-driven optimization directly in the feature space, our method increases optimization efficiency and the ability to avoid being trapped in local minima. In the second part of this thesis, a multivalued neural network is proposed to solve the non-uniqueness (multivalued) problem in inverse modeling. Our proposed technique can be effectively applied to parameter extraction of microwave filters. We propose a multivalued neural network inverse modeling technique to associate a single set of electrical parameters with multiple sets of geometrical or physical parameters. One set of geometrical or physical parameters is called one value of our proposed inverse model. Our proposed multivalued neural network is structured to accommodate multiple values for the model output.We also propose a new training error function to focus on matching each training sample using only one value of our proposed inverse model, while other values are ii free and can be trained to match other contradictory samples. In this way, our pro-
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