WLAN/WIMAX patch antenna design using artificial neural networks

Abstract

In this article, we propose a coplanar waveguide (CPW) antenna for WLAN/WIMAX applications using the artificial neural network (ANN) technique. In order to attain the correct resonant frequencies for WLAN/WIMAX applications, we inserted three slots in the radiating patch to form an inverted U-shaped slot. To achieve antenna performance in the standard WLAN/WIMAX frequency band, we adjusted the position and dimensions of the inverted U-shaped slot. The ANN method helped to better predict the dimensions of the inverted U-shaped slot. To collect the datasets used for training the ANN structure, we wrote a Visual Basic (VB) script in order to control the HFSS simulator using MATLAB. After training the network, the dimensions of the inverted U-shaped slot could easily be predicted for the desired resonant frequencies. The predicted ANN dimensions were used to design the antenna using both CST and HFSS electromagnetic simulators. To better evaluate the CPW antenna performance, we study and discuss the reflection coefficient, radiation pattern, voltage standing wave ratio (VSWR), current distribution, gain, and radiation efficiency. A prototype of the proposed antenna was fabricated; the obtained results show |S11| ≤ − 10 dB in the frequency bands of 2.31–2.43 GHz, 3.10–3.44 GHz, and 3.94–5.88 GHz, which make the antenna suitable for WLAN/WIMAX applications.

Design and Optimization of Sierpinski Carpet Fractal Antenna Using Artificial Neural Network (Ann) and High Frequency Structure Simulator (Hfss) Computational Techniques

“…The neural network has become a widely recognized mathematical structure, forming the foundation of data-driven models [8][9][10]. These networks, inspired by the human brain, are a category of machine learning models characterized by interconnected nodes called neurons, organized into layers.…”