Optimization Of Antenna Arrays Research Articles

Design, Analysis and Optimization of A Slotted Microstrip Patch Antenna Array at Frequency 5.25 GHz for WLAN-SDMA System

The design of array antenna is vital study for today's Wireless communication system to achieve higher gain, highly directional beam and also to counteract the effect of fading while signal propagates through various corrupted environments. In this study, the design and analysis of a (2x2) microstrip patch antenna array is introduced. It is designed to function in the 5.25 GHz which corresponds to IEEE 802.11a (VSWR -9.5dB) and antenna gain of 13.88 dBi have been achieved. Both numerical and experimental studies have been carried out to optimize the distance between the antenna elements. The measured results have a good agreement with that of simulated results. The numerical study has been done by using Zeland make IE3D electromagnetic simulator and measurements have been done with the help of Agilent make N5230A, network analyzer. Index Terms: Antenna array, Antenna gain, Slotted microstrip patch, Fringing field, Wireless LAN, IE3D electromagnetic simulator, antenna feed point, Array optimization.

A Comparative Study of Optimization Techniques in Adaptive Antenna Array Processing: The Bacteria-Foraging Algorithm and Particle-Swarm Optimization

This paper presents a comparative study between the Bacteria-Foraging (BF) and Particle-Swarm Optimization (PSO) algorithms, and their application to the antenna-array optimization problem. The performance of the Bacteria-Foraging Algorithm is studied by varying its different parameters in beamforming and null-steering problems. Null steering along with sidelobe suppression is also done for various degrees of complexity. A generic cost function is developed, the parameters of which can be controlled to meet the requirements of the particular application. The two algorithms are compared for null depth, average sidelobe level, and rate of convergence for different numbers of interference signals. The performance of these methods are compared for output noise power for the same noise inputs. Results are shown for a linear dipole antenna-array system.

Optimization of circular antenna arrays of isotropic radiators using simulated annealing

This paper presents the design optimization of circular antenna arrays of isotropic radiators using simulated annealing. The problem has been formulated to achieve a desired value of sidelobe level and a minimum possible value of beamwidth. This is accomplished by jointly optimizing the excitation amplitude and spacing between elements. Simulation examples have been given and comparison has been carried out with particle swarm optimization method.

A Genetic Algorithm Applied for Optimization of Antenna Arrays Used in Mobile Radio Channel Characterization Devices

This paper presents a proposed method for synthesizing scenario-dependent channel characterization system arrays that are adapted to the mobile radio environment where measurements take place. The criterion for synthesizing the array is the minimization of the angular Cramer-Rao lower bound, which is the lower bound of the mean square estimation error (MSEE) for an unbiased estimator. The core of the process is a genetic algorithm (GA) that seeks for the most advantageous array topology for each scenario. Simulation results show that the method succeeds in synthesizing feasible antenna arrays that have increased direction of arrival (DoA) estimation accuracy and are superior to the arrays typically used during actual measurement campaigns.

On the Use of Random Variables in Particle Swarm Optimizations: A Comparative Study of Gaussian and Uniform Distributions

The particle swarm optimization (PSO), now widely used in the electromagnetics community, is a robust evolutionary method based on the property of swarm intelligence. This paper focuses on the random variable effect in the PSO algorithm, and two random distribution functions, namely, the uniform distribution and Gaussian distribution, are studied and compared in details. It is revealed through the statistic analysis that the Gaussian distributed random variables increase the efficiency of the PSO algorithm as compared to the widely used uniformly distributed random variables. This conclusion has been demonstrated through both a mathematical benchmark function and an antenna array optimization.

Imperialist competitive algorithm for minimum bit error rate beamforming

In this paper, the recently introduced optimisation strategy, imperialist competitive algorithm (ICA) is used to design an optimal antenna array which minimises the error probability for binary phase shift keying modulation, called minimum bit error rate (MBER) beamforming. ICA is used to deal with the high complexity and high dimensionality of this challenging problem which can not be easily solved by gradient-based methods. The results are compared to that of both a genetic algorithm (GA) and the optimal array obtained by a properly set gradient based algorithm. Comparison shows that in contrast with GA, ICA leads to the array with error probability which is very close to the optimal value. Also, being faster than GA, ICA minimises the cost function with more consistency. The total results show that ICA is a powerful and reliable tool for solving complex optimisation problems such as MBER beamforming.

Optimization of the Yagi-Uda Antenna for Gain, Impedance, and Sidelobes Using Simulated Annealing

The Yagi-Uda antenna is being widely used for VHF/UHF applications. Depending on the particular application, there are different design goals to be met. Several techniques, such as gradient-based method, genetic algorithms, and particle swarm optimization, have been used for optimization of the Yagi-Uda antenna for different design objectives. Simulated annealing (SA) is a stochastic global optimization technique, which has been used successfully for the optimization of antenna arrays. In this article, the use of SA for optimization of gain, impedance, and sidelobe level of the pattern of the Yagi-Uda antenna has been presented. To evaluate the performance of the designs, a method of moments code NEC2 has been used. The results obtained show superiority of SA over other methods.

Reviews and Abstracts [review of Genetic Algorithms in Electromagnetics (Haupt, R.L. and Werner, D.H.; 2007

As the first authored book on genetic algorithms (GAs) in electromagnetics, this text is the most coherent introduction to the topic available today. The book is organized primarily by electromagnetic topic, which makes it easy to understand the state of the art in several fields of current research. Topics covered include: local optimization algorithms and simulated annealing; an introduction to genetic algorithms; the application of genetic algorithms to antenna-array optimization; the use of genetic algorithms for the control of adaptive and smart antennas; the wire antenna; the optimization of aperture and microstrip antennas; scattering; and parameter choices in genetic algorithms. Although not a comprehensive introduction to genetic algorithms themselves, the book will be indispensable to electromagnetic-device designers trying to build their optimization practice. The thorough reference list will help even the most seasoned GA researcher learn the state of the art, and the book's examples will inspire even greater creativity in the application of genetic algorithms to electromagnetics.

Design, optimization, and validation of a planar nine-element quasi-Yagi antenna array for X-band applications [antenna designer's notebook

A planar antenna array, based on nine identical quasi-Yagi elements, is presented in this paper. Contrary to conventional designs, the feeding network is composed of three-way power dividers, which are carefully designed in order to provide synchronized output signals over a wide frequency range. The use of spline-shaped microstrip lines suppresses coupling effects by parallel lines. Thus, signal distortions within the feeding network are minimized. Compared to conventional designs with eight elements, the additional antenna element will enhance the gain of the array, which is between 10.5 dBi and 13 dBi in the X band. The input return loss of the antenna array is below -15 dB over most parts of the relevant spectrum, with a total bandwidth of 45%. The planar antenna array is well suited as a radiating element in linear phased arrays for multifunction radars, including SAR and MTI. Additionally, the suitability of this antenna for phased-array applications is studied by an experimental setup consisting of five antenna plates.

APPLICATION OF ELECTROMAGNETISM-LIKE ALGORITHM TO PHASE-ONLY SYNTHESES OF ANTENNA ARRAYS

In this paper, a novel stochastic approach, i.e., the electromagnetism-like (EM-like) algorithm, is applied to phase-only syntheses of antenna arrays. The goal is to minimize the pattern sidelobe under null-steering constraints by phase-only adjusting. The mechanism of EM-like algorithm results from the Coulomb's Law of Electromagnetics. It does not require gradient calculations, and can automatically converge at a good solution through the virtual charge of each particle. Simulation results show that the EM-like algorithm can well treat the phase-only optimization of antenna arrays. Although the null-steering constraints will affect the optimization in sidelobe reduction, it can be easily included in the EM-like based optimization scheme. The EM-like algorithm can be applied not only to problems of antenna arrays, but also to many other nonlinear optimization problems in electromagnetic waves.

Optimization of Yagi-Uda Antenna Using Simulated Annealing

Simulated annealing (SA) is a stochastic global optimization technique, which has been used successfully for optimization of antenna arrays. In this paper, the use of SA for optimization of gain, impedance and bandwidth of the Yagi-Uda antenna has been presented. To evaluate the performance of designs, a method of moments code NEC2 has been used. Comparative results indicate superiority of using SA over other methods.

Optimization of antenna arrays based on neurocomputers

In the present article the application of neural networks for solving the problem of optimization of antenna arrays is considered; an efficient algorithm for maximization of the energy parameters of antenna arrays is proposed; numerical investigations are carried out and a neural network optimization algorithm is compared to well-known algorithms.

Design, optimization, and validation of a planar nine-element Quasi-Yagi antenna array for X-band applications [Antenna designer's Notebook

A planar antenna array, based on nine identical quasi-Yagi elements, is presented in this paper. Contrary to conventional designs, the feeding network is composed of three-way power dividers, which are carefully designed in order to provide synchronized output signals over a wide frequency range. The use of spline-shaped microstrip lines suppresses coupling effects by parallel lines. Thus, signal distortions within the feeding network are minimized. Compared to conventional designs with eight elements, the additional antenna element will enhance the gain of the array, which is between 10.5 dBi and 13 dBi in the X band. The input return loss of the antenna array is below -15 dB over most parts of the relevant spectrum, with a total bandwidth of 45%. The planar antenna array is well suited as a radiating element in linear phased arrays for multifunction radars, including SAR and MTI. Additionally, the suitability of this antenna for phased-array applications is studied by an experimental setup consisting of five antenna plates.

An Autopolyploidy-Based Genetic Algorithm for Enhanced Evolution of Linear Polyfractal Arrays

There has been considerable recent interest in techniques for the optimization of large-N antenna arrays. Unfortunately, the successful development of such techniques has been hindered by the large number of independent parameters that must be optimized and the complexity of the calculations needed for the electromagnetic evaluation of large-N arrays. One promising new design methodology for large-N arrays which has recently been introduced is based on properties of a subset of fractal-random arrays called polyfractal arrays. Polyfractal arrays have many embedded self-similar structures, thereby allowing very large and seemingly complex array layouts to be described with only a small set of independent parameters. In addition, by effectively utilizing the self-similarity of polyfractal arrays, a considerable reduction can be achieved in the amount of time required to evaluate the radiation patterns of large-N arrays. This paper introduces a type of nature-based design process that applies a specially formulated genetic algorithm (GA) technique to evolve optimal polyfractal array layouts. The most unique aspect of this optimization technique is a new autopolyploidy-based chromosome expansion that maximizes the efficiency of the GAs. Simple polyfractal geometries are used in the initial stage or first epoch of the optimization because the number of independent parameters is small and the computation times are relatively fast. After the optimization converges for the first epoch, more complicated descriptions of these polyfractal arrays are introduced to provide additional independent parameters for the optimizer as it progresses through later epochs of evolution. This process has been shown to be very effective in creating optimized large-N arrays, the largest example considered here being a 1616-element linear array with a -24.30-dB sidelobe level and a 0.056deg half-power beamwidth

On the design of Chebyshev endfire arrays subject to a performance index

A numerical method for the optimization of Chebyshev endfire antenna arrays, under a nonlinear constraint, is presented. Starting from a set of initial parameters (number of elements, equidistance, side lobe level, half-power beamwidth) we proceed to the design procedure subject to a desired value of a performance index. The method is based on the least squares technique where the constraint is applied through a Lagrangian multiplier. The resulting Chebyshev-like pattern fulfills the constraint whereas it loses the initial characteristic of equal side lobes. The presented method is applied for radiation efficiency and quality factor indices and gives remarkable results for different Chebyshev arrays.

Modelling and Simulation of Direction-Selective and Frequency-Selective Mobile Radio Channels

Summary This paper presents a new approach to the design of simulation models for spatio-temporal mobile radio channels, which are important for the design, optimization, and test of antenna array based mobile communication systems. The proposed spatio-temporally correlated simulation model is completely deterministic and enables the emulation of the desired fading behaviour with comparably little effort. Moreover, it can be configured flexibly to simulate not only different kinds of theoretical channels with given spatial, temporal, and frequency correlation properties, but also real-world fading channels for mobile communication systems with adaptive antennas.

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A structured gradient algorithm for adaptive beamforming

The constrained least‐mean‐square (LMS) algorithm uses a noisy estimate of the required gradient to adaptively estimate the weights of an optimal antenna array. The gradient is estimated by multiplying the array output with the array receiver outputs. An alternative scheme for estimating the required gradient is proposed in this paper. The new scheme uses a structured estimate of the array correlation matrix to estimate the gradient. This structure reflects the structure of the exact array correlation matrix and is obtained by a spatial averaging of the elements of the noisy array correlation matrix used in the standard algorithm. It is shown that the estimated gradient is unbiased and the array weights converge to the optimal weights in the mean sense. Furthermore, the weights estimated by the structured gradient algorithm are less noisy than those estimated by the standard algorithm.

The application of a biquadratic programming method to phase only optimization of antenna arrays

Antenna array optimization by a biquadratic programming method is formulated with variable-phase (analog and digital) and constant amplitude excitation. The method can optimize an antenna performance index expressible as a ratio of Hermitian quadratic forms. Power gain optimization and least-squares pattern synthesis with a specified gain are illustrated. Linear, planar, and circular arrays with dipole or circular loop elements are optimized, taking mutual coupling effects between antenna elements into account. The method shows much more rapid convergence characteristics than the Rosenbrock (1960) and Fletcher-Power (1963) methods. >

An algorithm for the empirical optimization of antenna arrays

A numerical technique is presented to optimize the performance of arbitrary antenna arrays under realistic conditions. An experimental-computational algorithm is formulated in which <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</tex> -dimensional minimization methods are applied to measured data obtained from the antenna array. A numerical update formula is used to induce partial derivative information without requiring special perturbations of the array parameters. The algorithm provides a new design for the antenna array, and the method proceeds in an iterative fashion. Test case results are presented showing the effectiveness of the algorithm.