Physical Bounds of Antennas

Gustafsson, Mats; Tayli, Doruk; Cismasu, Marius

doi:10.1007/978-981-4560-75-7_18-1

Cited by 24 publications

(28 citation statements)

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“…Stored energy for antennas is a concept which arose by necessity to calculate the Q factor [ Chu , ; Ohira , ; Gustafsson et al , ; Volakis et al , ; Yaghjian and Best , ]. The Q factor measures how well an oscillating system stores energy as opposed to dissipating it.…”

Section: Stored Energy Q Factor and State‐space Modelsmentioning

confidence: 99%

“…The Q factor measures how well an oscillating system stores energy as opposed to dissipating it. The Q factor for an antenna tuned to resonance is defined as [ Gustafsson et al , ; Volakis et al , ; Yaghjian and Best , ]

Q = \frac{2 ω \max {W_{e}, W_{m}}}{P_{d}} = ω \frac{W + | W_{m} - W_{e} |}{P_{d}},

where W = W e + W m , W e , and W m denote the stored electromagnetic, electric, and magnetic energies, respectively, ω is the angular frequency, and P d is the dissipated power. The dissipated power P d and energy difference W m − W e are well defined and follow directly from Poynting's theorem [ Geyi , ; Gustafsson and Jonsson , ].…”

Section: Stored Energy Q Factor and State‐space Modelsmentioning

confidence: 99%

“…Because of its relation to the bandwidth the Q factor is an important parameter for antenna design. Thus, it has become imperative to define stored energy for antennas [ Chu , ; Yaghjian and Best , ; Volakis et al , ; Gustafsson et al , ; Capek et al , ; Schab et al , ]. However, this is not trivial as electromagnetic fields, current densities, and circuit models can give different interpretations of the stored energy, see Figure .…”

Section: Stored Energy Q Factor and State‐space Modelsmentioning

confidence: 99%

“…The classical way of interpreting stored energy is as the energy stored in the fields that radiate from the antenna but do not escape its vicinity, see Figure b. This naturally leads to the subtraction calculation method, where the stored energy, W F , is calculated by subtracting the power flow or the far‐field power from the total energy [ Collin and Rothschild , ; Fante , ; Yaghjian and Best , ; Gustafsson et al , ; Volakis et al , ]. Subtraction of the far‐field term yields

W_{normalF} = \frac{1}{4} \int_{{double-struckR}_{normalr}^{3}} ε | E (r) |^{2} + μ | H (r) |^{2} - 2 ε \frac{| F (\overset{r}{true}) |^{2}}{r^{2}} normaldV,

where ε is the permittivity, μ is the permeability, E and H are the electric and magnetic fields, and

F (\overset{r}{true})

is the electric far field; F ∼ e j k r r E as

r \to \infty

with r =| r | and

\overset{r}{true} = r / r

.…”

Section: Stored Energy Q Factor and State‐space Modelsmentioning

confidence: 99%

“…Stored energy is instrumental for antenna analysis in terms of the Q factor [ Collin and Rothschild , ; Yaghjian and Best , ; Gustafsson et al , , ; Volakis et al , ; Capek et al , ; Chu , ; Schab et al , ]. In Yaghjian [], Yaghjian et al [], and Hansen et al [] stored energy is considered for small antennas composed of dispersive or lossy media, embedded in free space.…”

Section: Introductionmentioning

confidence: 99%

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State‐space models and stored electromagnetic energy for antennas in dispersive and heterogeneous media

Gustafsson

Ehrenborg

2017

Radio Science

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Accurate and efficient evaluation of the stored energy is essential for Q factors, physical bounds, and antenna current optimization. Here it is shown that the stored energy can be estimated from quadratic forms based on a state‐space representation derived from the electric and magnetic field integral equations. The derived expressions are valid for small antennas embedded in temporally dispersive and inhomogeneous media. The quadratic forms also provide simple single frequency formulas for the corresponding Q factors. Numerical examples comparing the different Q factors are presented for dipole and meander line antennas in conductive, Debye, and Lorentz media for homogeneous and inhomogeneous media. The computed Q factors are also verified with the Q factor obtained from the stored energy in Brune synthesized circuit models.

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Section: Stored Energy Q Factor and State‐space Modelsmentioning

confidence: 99%

Q = \frac{2 ω \max {W_{e}, W_{m}}}{P_{d}} = ω \frac{W + | W_{m} - W_{e} |}{P_{d}},

Section: Stored Energy Q Factor and State‐space Modelsmentioning

confidence: 99%

Section: Stored Energy Q Factor and State‐space Modelsmentioning

confidence: 99%

W_{normalF} = \frac{1}{4} \int_{{double-struckR}_{normalr}^{3}} ε | E (r) |^{2} + μ | H (r) |^{2} - 2 ε \frac{| F (\overset{r}{true}) |^{2}}{r^{2}} normaldV,

where ε is the permittivity, μ is the permeability, E and H are the electric and magnetic fields, and

F (\overset{r}{true})

is the electric far field; F ∼ e j k r r E as

r \to \infty

with r =| r | and

\overset{r}{true} = r / r

.…”

Section: Stored Energy Q Factor and State‐space Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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State‐space models and stored electromagnetic energy for antennas in dispersive and heterogeneous media

Gustafsson

Ehrenborg

2017

Radio Science

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

Montaser

Mahmoud

2017

Int J RF Microw Comput Aided Eng

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In this article, the antenna partial gain to Q-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 Q-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%. K E Y W O R D S current optimization, Nelder-Mead algorithm, patch antenna, very simple optimization Int J RF Microw Comput Aided Eng. 2017;27:e21104.

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Physical Bounds of Antennas

Gustafsson¹,

Tayli²,

Cismasu³

2015

Handbook of Antenna Technologies

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Design of small antennas is challenging because fundamental physics limits the performance. Physical bounds provide basic restrictions on the antenna performance solely expressed in the available antenna design space. These limits offer antenna designers a-priori information about the feasibility of antenna designs and a figure of merit for different designs. Here, an overview of physical bounds on antennas and the development from circumscribing spheres to arbitrary shaped regions and embedded antennas are presented. The underlying assumptions for the methods based on circuit models, mode expansions, forward scattering, and current optimization are illustrated and their pros and cons are discussed. The physical bounds are compared with numerical data for several antennas.

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Physical Bounds of Antennas

Cited by 24 publications

References 100 publications

State‐space models and stored electromagnetic energy for antennas in dispersive and heterogeneous media

State‐space models and stored electromagnetic energy for antennas in dispersive and heterogeneous media

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

Physical Bounds of Antennas

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