Neural network applications in smart antenna arrays: A review

Rawat, Abhishek; Yadav, Ram Narayan; Shrivastava, S. C.

doi:10.1016/j.aeue.2012.03.012

Cited by 110 publications

(52 citation statements)

References 39 publications

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“…In recent years, a number of studies are done on performance comparison of ANNs for a range of applications [19,21,26,[33][34][35][36][37][38][39]. The performance of the proposed models is compared on the basis of two different measures that is, (1) mean absolute error (MAE), which gives the quality of training the model.…”

Section: General Regression Neural Network Parametersmentioning

confidence: 99%

Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

Dhaliwal

Pattnaik

2013

Advances in Artificial Neural Systems

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Artificial neural networks due to their general-purpose nature are used to solve problems in diverse fields. Artificial neural networks (ANNs) are very useful for fractal antenna analysis as the development of mathematical models of such antennas is very difficult due to complex shapes and geometries. As such empirical approach doing experiments is costly and time consuming, in this paper, application of artificial neural networks analysis is presented taking the Sierpinski gasket fractal antenna as an example. The performance of three different types of networks is evaluated and the best network for this type of applications has been proposed. The comparison of ANN results with experimental results validates that this technique is an alternative to experimental analysis. This low cost method of antenna analysis will be very useful to understand various aspects of fractal antennas.

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Section: General Regression Neural Network Parametersmentioning

confidence: 99%

Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

Dhaliwal

Pattnaik

2013

Advances in Artificial Neural Systems

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“…In this section, it is important to explain clearly the artificial neural network (ANN) as a powerful computational model that has been successfully used to simulate the features and behaviors of the antenna arrays [9,16,17]. 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%

“…Several cases that still need to be addressed for solving practical problems using artificial neural network (ANN) approach are given [9,15,17]. This paper is organized as follows: in Section 2 the theoretical background of the array factor analysis including pattern synthesis is reminded, in particular uniform concentric circular antenna arrays.…”

Section: Introductionmentioning

confidence: 99%

“…But, artificial neural network (ANN) have been studied in various application like pattern recognition systems, and have been exploited for inputoutput mapping, for system identification, for adaptive prediction, etc.... Therefore, we are interested to present in this paper the neural networks method that will be applied to the array pattern synthesis, highlighting their most important features and distinctive characters [8,9].…”

Section: Introductionmentioning

confidence: 99%

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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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“…The problem of antenna monitoring has been addressed by several researchers. Over the past few decades, many monitoring techniques have been proposed for the detection of faults in radiation patterns of antenna arrays, which are based on Bayesian compressive sensing (Oliveri, Paolo, & Massa, 2012), NN (Castaldi, Pierro, & Pinto, 2000;Rawat, Yadav, & Shrivastava, 2012;Vakula & Sarma, 2009), GA (Rodriguez, Ares, Palacios, & Vassal'lo, 2000;Yeo & Lu, 1999), case-based reasoning (Iglesias et al, 2008), bacteria foraging optimization (Acharya, Patnaik, & Choudhury, 2011), exhaustive searches (Rodriguez-Gonzalez, Ares-Pena, Fernandez-Delgado, Iglesias, & Barro, 2009), support vector machine (Rajagopalan, Joshi, & Gudla), and distributional approach (Buonanno, D'Urso, Cicolani, & Mosca, 2009).…”

Section: Introductionmentioning

confidence: 99%

Monitoring linear antenna arrays using an exponentially weighted moving average-based fault detection scheme

Harrou

Nounou

2014

Systems Science & Control Engineering

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The evolution of modern wireless communications systems has dramatically increased the demand for antenna arrays. An antenna array with certain radiation characteristics is often desired. However, the actual radiation pattern of an antenna array changes when faults are introduced in the array. In this paper a statistical fault detection methodology based on the exponentially weighted moving average (EWMA) control scheme is proposed to detect possible faulty radiation patterns in linear antenna arrays. The proposed method detects the faults based on deviation in the radiation pattern from the desired ones. The difference between synthesized radiation pattern obtained using the Minimax algorithm and the measured pattern can be used as an indicator about the existence or absence of faults. To assess the fault detection abilities of the EWMA control scheme, three case studies are considered, one involving a complete failure in one element in the array, one involving partial failure in two elements, and one involving degradation caused by random noise due to interference and other factors. The simulation results for all cases show the effectiveness of the proposed EWMA fault detection method.

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Neural network applications in smart antenna arrays: A review

Cited by 110 publications

References 39 publications

Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

Artificial Neural Network Analysis of Sierpinski Gasket Fractal Antenna: A Low Cost Alternative to Experimentation

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

Monitoring linear antenna arrays using an exponentially weighted moving average-based fault detection scheme

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