Neural methods for antenna array signal processing: a review

Du, Ke-Lin; Lai, A.K.Y.; Cheng, K.-K.M.; Swamy, M.N.S.

doi:10.1016/s0165-1684(01)00185-2

Cited by 95 publications

(38 citation statements)

References 84 publications

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“…Neural network approaches have been successfully applied to real-time beamforming (Du et al 2002). Since the robust LCMP problem (70) is a complex valued one, it is first necessary to convert it into a real valued optimization …”

Section: Neural Network Approachesmentioning

confidence: 99%

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Kamel

Xia

2008

Cogn Neurodyn

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Constrained optimization problems arise in a wide variety of scientific and engineering applications. Since several single recurrent neural networks when applied to solve constrained optimization problems for real-time engineering applications have shown some limitations, cooperative recurrent neural network approaches have been developed to overcome drawbacks of these single recurrent neural networks. This paper surveys in details work on cooperative recurrent neural networks for solving constrained optimization problems and their engineering applications, and points out their standing models from viewpoint of both convergence to the optimal solution and model complexity. We provide examples and comparisons to shown advantages of these models in the given applications.

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Section: Neural Network Approachesmentioning

confidence: 99%

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Kamel

Xia

2008

Cogn Neurodyn

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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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“…TDFN and ADALINE network [11][12][13] use tapped delay line to perform temporal processing. Both neural network structures are used to predict the next value of the spatial signature vector as the downlink weight vector.…”

Section: Neural Network Modelingsmentioning

confidence: 99%

“…The aim of present work is to predict downlink weight vectors via time delay feedforward neural network (TDFN) modeling, adaptive linear neuron (ADALINE) network modeling [9][10][11][12], and autoregressive (AR) modeling of uplink spatial signature vectors. We compare the performance of these models under varying mobile speed (V) and prediction filter (delay) order (P).…”

Section: Introductionmentioning

confidence: 99%

Autoregressive and Neural Network Model Based Predictions for Downlink Beamforming

Yiğıt

Kavak

Ertunç

2004

Lecture Notes in Computer Science

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Abstract. In Time-Division-Duplex (TDD) wireless communications, downlink beamforming performance of a smart antenna system can be degraded due to variation of spatial signature vectors in vehicular scenarios. To mitigate this, downlink beams must be adjusted according to changing propagation dynamics. This can be achieved by modeling spatial signature vectors in the uplink period and then predicting them for new mobile position in the downlink period. This paper examines time delay feedforward neural network (TDFN), adaptive linear neuron (ADALINE) network and autoregressive (AR) filter to predict spatial signature vectors. We show that predictions of spatial signatures using these models provide certain level of performance improvement compared to conventional beamforming method under varying mobile speed and filter (delay) order conditions. We observe that TDFN outperforms ADALINE and AR modeling for downlink SNR improvement and relative error improvement with high mobile speed and higher filter order/delay conditions in fixed Doppler case in multipaths.

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Neural methods for antenna array signal processing: a review

Cited by 95 publications

References 84 publications

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

Cooperative recurrent modular neural networks for constrained optimization: a survey of models and applications

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

Autoregressive and Neural Network Model Based Predictions for Downlink Beamforming

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