Radiation Pattern Synthesis Using Hybrid Fourier- Woodward-Lawson-Neural Networks for Reliable MIMO Antenna Systems

TL;DR

This paper introduces a hybrid Fourier-Woodward-Lawson-Neural Network approach for synthesizing MIMO antenna arrays with optimized radiation patterns, reducing sidelobe levels and interference for reliable wireless communication.

Contribution

The paper presents a novel hybrid neural network and Fourier method for antenna array synthesis, providing analytical equations and improved performance for MIMO systems.

Findings

Effective reduction of sidelobe levels in antenna patterns

Enhanced reliability and interference reduction in MIMO systems

Analytical synthesis equations derived from the hybrid method

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.