Phase-only adaptive nulling with neural networks for antenna array synthesis

Ghayoula, Ridha; Fadlallah, Najib; Gharsallah, Ali; Rammal, M.

doi:10.1049/iet-map:20070256

Cited by 39 publications

(37 citation statements)

References 27 publications

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“…The angular‐dependent vector f n ( θ , φ ) gives the directional characteristics of the n th element electric field :

f_{n} (θ, φ) = I_{n} f (θ, φ)

f(θ,φ) is called the ‘pattern function’ of the element with the complex excitation of the n th element of the array.

E (r) ≅ - jϖμ \frac{e^{- italicjβr}}{4 πr} f (θ, φ) \sum_{n = 1}^{N} I_{n} e^{jβ r_{n} cos ξ_{n}}

…”

Section: Electromagnetic Optimization Using Taguchi's Methodsmentioning

confidence: 99%

“…rr can be set between 0.5 and 1 according to the problem . So, for the ( i + 1) th iteration ,

L D_{i + 1} = RR (i) \times L D_{1} = r r^{i} \times L D_{1}

…”

Section: Electromagnetic Optimization Using Taguchi's Methodsmentioning

confidence: 99%

“…For a 10‐element symmetrical array, the array factor can be written as :

AF (θ) = 2 \sum_{n = 1}^{5} A (n) e^{jφ (n)} cos [kd (n) cos θ]

where k is the wave number; A ( n ), d ( n ) = λ/2, and φ ( n )=0 are the excitation amplitude, location, and phase of the n th element, respectively.…”

Section: Electromagnetic Optimization Using Taguchi's Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Phased arrays in communication system based on Taguchi‐neural networks

Smida

Ghayoula

Nemri

et al. 2013

Int J Communication

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SUMMARY Phased antenna array design is one of the most important electromagnetic optimization problems. This research combined the Taguchi method and artificial intelligence methods, used them as the prediction tool in designing parameters for the communication system, and then constructed a set of the optimal parameter analysis flow and steps. In this paper, we present an application of artificial neural networks in the electromagnetic domain. We particularly look at the multilayer perceptron network, which has been the most used of artificial neural networks architectures both in the electromagnetic domain and in the Taguchi optimization technique and describes the Taguchi method to optimize the excitations elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control. This paper investigates how the implementation of the signal processing in hardware affects the performance of the adaptive array antenna. The investigation is confined to uplink or receive antenna array only. Results of a prototype of antenna array with feeding values designed using the proposed techniques are also presented. Copyright © 2013 John Wiley & Sons, Ltd.

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“…The angular‐dependent vector f n ( θ , φ ) gives the directional characteristics of the n th element electric field :

f_{n} (θ, φ) = I_{n} f (θ, φ)

f(θ,φ) is called the ‘pattern function’ of the element with the complex excitation of the n th element of the array.

E (r) ≅ - jϖμ \frac{e^{- italicjβr}}{4 πr} f (θ, φ) \sum_{n = 1}^{N} I_{n} e^{jβ r_{n} cos ξ_{n}}

…”

Section: Electromagnetic Optimization Using Taguchi's Methodsmentioning

confidence: 99%

“…rr can be set between 0.5 and 1 according to the problem . So, for the ( i + 1) th iteration ,

L D_{i + 1} = RR (i) \times L D_{1} = r r^{i} \times L D_{1}

…”

Section: Electromagnetic Optimization Using Taguchi's Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Phased arrays in communication system based on Taguchi‐neural networks

Smida

Ghayoula

Nemri

et al. 2013

Int J Communication

Self Cite

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“…The benefit of neural networks includes many areas of research that involves building a model of the brain and training the latter model to recognize certain types of patterns especially for the synthesis and optimization of antenna arrays [14,15].…”

Section: Artificial Neural Network (Ann) Principlesmentioning

confidence: 99%

“…The goal is then to build a feedforward neural network with supervised learning that approximates the following array pattern's function [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Uniform and Concentric Circular Antenna Arrays Synthesis for Smart Antenna Systems Using Artificial Neural Network Algorithm

Hamdi¹,

Selma²,

Aguili³

2016

PIER B

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Abstract-Recently, researchers were interested in neural algorithms for optimization problems for several communication systems. This paper shows a novel algorithm based on neural technique presented to enhance the performance analysis of beam-forming in smart antenna technology using N elements for Uniform Circular Array (UCA) and Concentric Circular Array (CCA) geometries. To demonstrate the effectiveness and reliability of the proposed approach, simulation results are carried out in MATLAB. The radiators are considered isotropic, and hence mutual coupling effects are ignored. The proposed array shows a considerable improvement against the existing structures in terms of 3-D scanning, size, directivity, HPBW and SLL reduction. The results show that multilayer feed-forward neural networks are robust and can solve complex antenna problems. However, artificial neural network (ANN) is able to generate very fast the results of synthesis by using generalization with early stopping method. Important gain in the running time and memory used is obtained using this latter method for improving generalization (called early stopping). To validate this work, several examples are shown.

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Hybrid MUSIC Dolph–Chebyshev algorithm for a smart antenna system

Ghayoula

Smida

Gharsallah

et al. 2013

Int J Communication

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This paper presents practical design of a smart antenna system based on direction-of-arrival estimation and Dolph-Chebyshev beam forming. Direction-of-arrival estimation is based on the multiple signal classification algorithm for identifying the directions of the source signals incident on the sensor array comprising the smart antenna system. The smart antenna system design involves a hardware part, which provides real data measurements of the incident signals received by the sensor array. This paper presents the Dolph-Chebyshev method for the synthesis and design of antenna arrays with periodic element spacing. A Field-Programmable Gate Array implementation is presented for an antenna array application employing digital beamforming. The array comprises 10 elements, equal in number receiving radio frequency and intermediate frequency components, as well as a Spartan-3E Field-Programmable Gate Array-based unit, which is responsible for the control of the array. Low-cost and switched-beam, and fully adaptive antenna array suitable for 2-GHz applications are proposed in this paper.

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Phase-only adaptive nulling with neural networks for antenna array synthesis

Cited by 39 publications

References 27 publications

Phased arrays in communication system based on Taguchi‐neural networks

Phased arrays in communication system based on Taguchi‐neural networks

Uniform and Concentric Circular Antenna Arrays Synthesis for Smart Antenna Systems Using Artificial Neural Network Algorithm

Hybrid MUSIC Dolph–Chebyshev algorithm for a smart antenna system

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