Use of genetic algorithms in linear and planar antenna array synthesis based on Schelkunoff method

Recioui, Abdelmadjid; Azrar, Arab

doi:10.1002/mop.22510

Cited by 23 publications

(17 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Normalized array factor is max AF AF AF norm  (9) In simulation, particular directions of main beam and null are given as input for 16 elements linear antenna array. The value of inter-element spacing'd' is 0.5λ.…”

Section: Adaptive Beamforming Of Smart Antenna Using Cma and Tdcmamentioning

confidence: 99%

“…Numerical and experimental results for an eight elements linear smart antenna array using a set of electronically driven vector modulators, are described in [8]. To improve the performance of adaptive smart antenna, array synthesis methods are used in [9][10]. Genetic algorithm is coupled with the Schelkunoff synthesis method for uniform linear and planar array design [9].…”

Section: Introductionmentioning

confidence: 99%

“…To improve the performance of adaptive smart antenna, array synthesis methods are used in [9][10]. Genetic algorithm is coupled with the Schelkunoff synthesis method for uniform linear and planar array design [9]. Tchebyscheff distribution (TD) is used with beamforming constraint stability least mean square (CSLMS) algorithm for adaptive antenna [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Beamforming of Smart Antenna Using a Combined Tchebyscheff Distribution and Constant Modulus Algorithm

Baliarsingh¹,

Senapati²,

Deb³

et al. 2017

IJET

View full text Add to dashboard Cite

Abstract-The objective of this paper is to improve the performance of adaptive smart antenna using a hybrid algorithm combining constant modulus algorithm (CMA) and array synthesis method. Adaptive beamforming using Tchebyscheff distribution, an array synthesis method, along with constant modulus algorithm (TDCMA) is compared with the performances of beamforming using constant modulus algorithm only. Reduction of side lobe level is an important task to minimize unwanted interference for other users in mobile network. Side lobe level of about 9 dB lower than CMA algorithm is achieved using TDCMA algorithm. Investigations, presented here, may be useful for mobile network.

show abstract

Section: Adaptive Beamforming Of Smart Antenna Using Cma and Tdcmamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Beamforming of Smart Antenna Using a Combined Tchebyscheff Distribution and Constant Modulus Algorithm

Baliarsingh¹,

Senapati²,

Deb³

et al. 2017

IJET

View full text Add to dashboard Cite

show abstract

“…One needs a faster and reliable method to find the optimum solution. Nongradient based optimization methods such as genetic algorithms (GA) [1,2] and particle swarm optimization (PSO) [3] are introduced in array thinning and have proved to be useful. Haupt [4] used GA in process of thinning a linear array of 200 elements, and resulting sidelobe levels were lower than -18 dB in all cases shown.…”

Section: Introductionmentioning

confidence: 99%

Design and Thinning of Linear and Planar Antenna Arrays Using a Binary Teaching Learning Optimizer

Recioui

2016

Acta Phys. Pol. A

Self Cite

View full text Add to dashboard Cite

In this paper, a binary variant of the teaching learning optimization technique is used to the design and thinning of linear and planar arrays. The purpose of the optimization task is to enhance the ratio directivity/sidelobe level which turns out to be having two conflicting parameters. The binary variant of the teaching learning optimization technique searches the way of exciting some selected elements. The array thinning problem requires some elements to be excited with the others having no current in them. This is a binary (ON-OFF) problem that requires an optimization technique that can handle the binary variables. The teaching learning optimization has been proposed initially to handle real valued variables. The results show good agreement between the desired and calculated radiation patterns with reduction in resource usage in terms of power consumption.

show abstract

“…These two properties turn out, however, not to go hand in hand in the sense that optimizing for one of them affects the other. Hence, a compromise has to be considered by the design engineer while trying to optimize the array performance in terms of these two characteristics [7,8].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Circular Antenna Arrays Using a Differential Search Algorithm

Recioui

2015

Acta Phys. Pol. A

Self Cite

View full text Add to dashboard Cite

Circular and concentric circular antenna array patterns are synthesized to generate array factors with minimum side-lobe level and high directivity. The comprehensive learning differential search algorithm is used for synthesizing a one-and a three-ring circular and concentric circular antenna array with thirty elements. This synthesis is done by finding the optimum inter-element spacing of rings, phases and /or positions that give optimum side lobe level. The computational results show that side lobe level is reduced significantly in non-uniform case for concentric in comparison with a single ring.

show abstract

Use of genetic algorithms in linear and planar antenna array synthesis based on Schelkunoff method

Cited by 23 publications

References 11 publications

Beamforming of Smart Antenna Using a Combined Tchebyscheff Distribution and Constant Modulus Algorithm

Beamforming of Smart Antenna Using a Combined Tchebyscheff Distribution and Constant Modulus Algorithm

Design and Thinning of Linear and Planar Antenna Arrays Using a Binary Teaching Learning Optimizer

Optimization of Circular Antenna Arrays Using a Differential Search Algorithm

Contact Info

Product

Resources

About