Authors: Balwinder S. Dhaliwal, Suman Pattnaik, Shyam Sundar Pattnaik

Fractal Antenna Design using Bio-inspired Computing Algorithms

eBook: US $59 Special Offer (PDF + Printed Copy): US $101
Printed Copy: US $71
Library License: US $236
ISBN: 978-981-5136-36-4 (Print)
ISBN: 978-981-5136-35-7 (Online)
Year of Publication: 2023
DOI: 10.2174/97898151363571230101

Introduction

This book presents research focused on the design of fractal antennas using bio-inspired computing techniques. The authors present designs for fractal antennas having desirable features like size reduction characteristics, enhanced gain, and improved bandwidths. The research is summarized in six chapters which highlight the important issues related to fractal antenna design and the mentioned computing techniques. Chapters demonstrate several applied concepts and techniques used in the process such as Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO). The work aims to provide cost-effective and efficient solutions to the demand for compact antennas due to the increasing demand for reduced sizes of components in modern wireless communication devices.

A key feature of the book includes an extensive literature survey to understand the concept of fractal antennas, their features, and design approaches. Another key feature is the systematic approach to antenna design. The book explains how the IE3D software is used to simulate various fractal antennas, and how the results can be used to select a design. This is followed by ANN model development and testing for optimization, and an exploration of ANN ensemble models for the design of fractal antennas.

The bio-inspired computing techniques based on GA, PSO, and BFO are developed to find the optimal design of the proposed fractal antennas for the desired applications. The performance comparison of the given computing techniques is also explained to demonstrate how to select the best algorithm for a given bio-inspired design. Finally, the book explains how to evaluate antenna designs.

This book is a valuable resource for students (from UG to PG levels) and research scholars undertaking learning modules or projects on microstrip and patch antenna design in communications or electronics engineering courses.

Preface

The demand of the compact antennas is increasing continuously due to the requirement for reduced-size wireless communication devices. The use of fractal geometry for the design of small-size antennas is a modern trend. As closed-form expressions do not exist for fractal antennas, alternative methods of designing fractal antennas are needed. The use of bio-inspired computing techniques like Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bacterial Foraging Optimization (BFO) is very appropriate in such cases. In the presented research work, these techniques have been used for parameter estimation and design optimization of fractal patch antennas. Therefore, the presented research works confines the fractal antennas & bio-inspired computing techniques to provide cost-effective & efficient solutions. An extensive literature survey is carried out to understand the concept of fractal antennas, their features and design approaches. Also, a number of research papers are reviewed on the applications of bio-inspired computing techniques for antenna design, especially fractal antenna design. The extracts of the literature survey presented in the book highlight these important issues.

Many fractal antenna geometries suitable for medical and communication applications have been proposed in the presented research work. The IE3D software has been used to simulate various fractal antennas, and the simulation results are obtained to analyze the performance of the selected antennas. The desired features are assessed from the S11 plots, gain plots and radiation patterns which are validated with experimental and analytical findings. The multilayer perceptron neural network, radial basis function neural network, and generalized regression neural network models are developed to estimate various parameters of the proposed fractal antennas. The performance of various ANN models has also been compared in order to find optimally suitable models. The use of ANN ensemble models for the design of fractal antennas is also explored, and it has been found that the ANN ensemble approach is better than the traditional ANN model approach. The different methods of developing ANN ensemble models are also presented. The bio-inspired computing techniques based on GA, PSO and BFO are developed to find the optimal design of the proposed fractal antennas for the desired applications. The performance comparison of the various bio-inspired computing techniques is also carried out to select the best algorithm. The use of ANN models as an objective function of optimization algorithms is also enumerated to design the presented fractal antennas. It has been observed that the developed bio-inspired computing techniques provide accurate solutions with a very small computational cost. The performance of the designed antennas is validated by fabricating prototypes and then performing experimental testing. The simulated results are compared with the experimental results, and good matching of simulated and experimental results is observed in almost all cases. The obtained results are also compared with the previously published results to validate the presented designs. The research work has resulted in the design of the fractal antennas having many desirable features like size reduction characteristics, enhanced gain, and improved bandwidths

This book contains six chapters which present the outcomes of the above-described research work.

Balwinder S. Dhaliwal
National Institute of Technical Teachers
Training and Research Chandigarh
India

Suman Pattnaik
Sri Sukhmani Institute of Engineering
and Technology Dera Bassi Punjab
India


&

S. S. Pattnaik
National Institute of Technical Teachers
Training and Research Chandigarh
India