Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Radiation Pattern Synthesis Using Hybrid Fourier- Woodward-Lawson-Neural
  Networks for Reliable MIMO Antenna Systems   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.

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The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org