Artificial Intelligence for Signal Processing and Wireless Communication

Abhinav Sharma, Arpit Jain, Ashwini Kumar Arya, Mangey Ram, Abhinav Sharma, Arpit Jain, Ashwini Kumar Arya, Mangey Ram

• Publisher: De Gruyter
• Year: 2022
• eBook ISBN: 9783110734720
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

This book focuses on artifi cial intelligence in the field of digital signal processing and wireless communication. The implementation of machine learning and deep learning in audio, image, and video processing is presented, while adaptive signal processing and biomedical signal processing are also explored through DL algorithms, as well as 5G and green communication. Finally, metaheuristic algorithms of related mathematical problems are explored.

Artificial intelligence applied to multi- and broadband antenna design

Satish K. Jain

Department of Electronics and Telecommunication Engineering, S.G.S. Institute of Technology and Science, Indore, India

Shobha Jain

Department of Mathematics, S.V.V.V, Indore, India

Acknowledgments: This work was supported by the Indian Space Research Organization (ISRO, India) and Shri G.S. Institute of Technology and Science, Indore (MP), India, through the Ministry of Human Resource Development (MHRD) India, under the Project TEQIP-II (NPIU).

Abstract

Looking into the multiband and broadband need of 5G wireless technology, researchers are trying hard to explore antennas design having multi- and broadband characteristics. Mostly, planar and vertical designs are suggested for these types of designs because various advantages like small size, low manufacturing cost, low profile, volume production, and conformability. It seems that stacked configurations, namely, antennas in vertical direction, may be one of the solutions for getting multiband and broadband features because the physique of the overall antenna does not expand horizontally. This is one of the most important features, which provides opportunity to construct a large array with limited space. However, the design process of stacked microstrip antenna using commercially available electromagnetic software is a cumbersome task because of ‘‘Generate and Test’’ approach involved. Furthermore, because stacked patch antenna involves large number of design parameters, therefore, the designer needs to optimize so many geometrical parameters. Hence, hundreds of simulations may require to reach at the final design. In this chapter, artificial intelligence algorithms such as artificial neural network, deep learning, and particle swarm optimization metaheuristic approach have been explored to systemize the entire design process of stacked patch antennas design. The proposed hybrid algorithms are stable and flexible computationally, which is able to provide accurate results. Developed antennas are useful for satellite, wireless local area network, and radar communication applications. The performance of the designed antennas has been verified through electromagnetic simulations done by IE3D software and experimental measurements accomplished through vector network analyzer. The close resemblance of the simulation and experimental results with the design specifications confirms the validity of the developed design methodology.

Keywords: artificial intelligence, particle swarm optimization, computer simulation technology, metaheuristic,

1 Introduction

A considerable research and development effort is being consistently devoted to the design and development of planar microstrip patch antennas [1, 2]. At present, rapid expansion in 5G mobile network and miniaturization of mobile handset indicates that the demand for microstrip antennas and arrays will increase. In wireless communication area, there is a need of integrating different communication services in a single device, thereby requiring multiband and broadband operations. In microstrip antenna technology, stacked configuration is one of the common and viable solutions for obtaining wideband and multiband characteristics because the size of the structure does not increase in the planar direction, making them available for use in antenna arrays. As on today, a number of commercial and freeware simulators are available for the analysis of these antennas [3]. Despite the current level of design sophistication, from designer’s view point, even today designers require to execute so many simulations numerically in order to extract the exact values of the antenna geometrical parameters. Hence, optimization becomes tedious numerically. Also, vender-provided electromagnetic software packages for antenna design occupy large memory even in gigabyte so computer resources get engaged heavily. In a nutshell, there is still a need for a tool for t...

Table of contents

• Title Page
• Copyright
• Contents
• Editors’ biographies
• Long short-term memory (LSTM) deep neural networks for sentiment classification
• Plant disease identification using IoT and deep learning algorithms
• A comprehensive study of plant pest and disease detection using different computer vision techniques
• Artificial intelligence applied to multi- and broadband antenna design
• Direction of arrival estimation using Lévy flight-based moth flame optimization algorithm
• NLP techniques, tools, and algorithms for data science
• Prediction of coronary artery disease using logistic regression
• Design of antenna with biocomputing approach
• Energy-efficient methods for railway monitoring using WSN
• Analysis of acoustic emission for milling operation using artificial neural networks
• Index