Paper Info
Title
Radiation pattern synthesis using hybrid fourier-woodward-lawson-neural networks for reliable mimo antenna systems.
Abstract
In this paper, we implement hybrid Woodward-Lawson Neural Networks and weighted Fourier method to synthesize antenna arrays. The neural networks (NN) is applied here to simplify the modeling of MIMO antenna arrays by assessing phases. The main problem is obviously to find optimal weights of the linear antenna array elements giving radiation pattern with minimum sidelobe level (SLL) and hence ameliorating the antenna array performance. To attain this purpose, an antenna array for reliable Multiple-Input Multiple-Output (MIMO) applications with frequency at 2.45 GHz is implemented. To validate the suggested method, many examples of uniformly excited array patterns with the main beam are put in the direction of the useful signal. The Woodward-Lawson Neural Networks synthesis method permits to find out interesting analytical equations for the synthesis of an antenna array and highlights the flexibility between the system parameters in input and those in output. The performance of this hybrid optimization underlines how well the system is suitable for a wireless communication and how it participates in reducing interference, as well.
Year
DOI
Venue
2017
10.1109/smc.2017.8123136
SMC
Field
DocType
Citations 
3G MIMO,Wireless,Radiation pattern,Computer science,MIMO,Antenna array,Electronic engineering,Fourier transform,Interference (wave propagation),Artificial neural network
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Elies Ghayoula100.34
Ammar Bouallégue25725.00
r ghayoula302.70
Jaouhar Fattahi4128.69
Pricop Emil505.75
Jean-Yves Chouinard619826.37