Phased array antenna controlled by neural network FPGA

Fournier, Jean-Luc; Titz, Diane; Ferrero, Fabien; Luxey, Cyril; Dekneuvel, Eric; Jacquemod, G.

doi:10.1109/lapc.2011.6114104

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Reconfigurable hardware platforms have a balance between hardware and software solutions exclusively, as they have the programmability of software with performance capacity approaching that of a custom hardware implementation [22]. Because of several attractive features like low cost as compared to MPLDs (mask programmable logic devices), easy implementation and reprogrammability, the FPGAs are being widely used for creating reconfigurable hardware of neural networks for different applications [23][24][25][26][27] but the literature on this approach for microwave applications domain is limited [28][29][30][31]. To et al [28] have described prototyping of a neuroadaptive smart antenna beam-forming algorithm using hardware-software approach by implementing the RBF neural network on FPGA platform.…”

Section: Introductionmentioning

confidence: 99%

“…They have then implemented the optimized model with 8-bit precision using Xilinx 8.1i, simulated with ModelSim SE 6.0, and downloaded and tested on Xilinx "XC3S500". Fournier et al [31] have implemented the neural networks model on FPGA platform for beam steering of an array of four microstrip patch antennas. The feeding structure of Butler matrix has been optimized using the momentum feature of advance design system (ADS) for 2.4 GHz frequency.…”

Section: Introductionmentioning

confidence: 99%

“…The feeding structure of Butler matrix has been optimized using the momentum feature of advance design system (ADS) for 2.4 GHz frequency. For controlling the beam angle, they have performed the training of the ANN model in MATLAB Simulink; coding for the trained model has been created using Xilinx system generator and, finally, it is downloaded on Xilinx' FPGA board [31]. The beauty of the present work lies in creating an FPGA-based reconfigurable hardware for a generalized neural model which is capable of computing seven different performance parameters.…”

Section: Introductionmentioning

confidence: 99%

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Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

Khan

Де

2014

Chinese Journal of Engineering

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In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA-) based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

Khan

Де

2014

Chinese Journal of Engineering

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“…These characteristic advantages of the neural network encourage us to use in adaptive beamforming. Identifying the inherent gains of neural networks, a number of literatures are available on neural network based model to calculate the weights of an adaptive array antenna [3]- [6].…”

mentioning

confidence: 99%

Single Perceptron Model for Smart Beam forming in Array Antennas

Senthilkumar

Pirapaharan

Hoole³

et al. 2016

IJECE

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<p>In this paper, a single neuron neural network beamformer is proposed. A perceptron model is designed to optimize the complex weights of a dipole array antenna to steer the beam to desired directions. The objective is to reduce the complexity by using a single neuron neural network and utilize it for adaptive beamforming in array antennas. The selection of nonlinear activation function plays the pivotal role in optimization depends on whether the weights are real or complex. We have appropriately proposed two types of activation functions for respective real and complex weight values. The optimized radiation patterns obtained from the single neuron neural network are compared with the respective optimized radiation patterns from the traditional Least Mean Square (LMS) method. Matlab is used to optimize the weights in neural network and LMS method as well as display the radiation patterns.</p>

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Applications and prospects of artificial intelligence in covert satellite communication: a review

Liu

Zeng

et al. 2023

Sci. China Inf. Sci.

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Phased array antenna controlled by neural network FPGA

Cited by 7 publications

References 6 publications

Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

Single Perceptron Model for Smart Beam forming in Array Antennas

Applications and prospects of artificial intelligence in covert satellite communication: a review

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