5G and Beyond Wireless Network, RF and Microwave Devices

This book provides a fundamental understanding of wireless communication, radio frequency (RF), and microwave devices for next-generation wireless communications. Various security issues and their solutions are included, and the book also covers machine learning techniques for advanced communications and antenna design. 5G and Beyond Wireless Network, RF and Microwave Devices: Modern Design and Optimization provides a thorough introduction to antenna design and 5G and B5G wireless networks, as well as cutting-edge technologies that aid in antenna/network design and development. Novel antenna designs and the latest technologies for 5G and future wireless networks are discussed, including multiple-input and multiple-output (MIMO) antennas, reconfigurable antennas, dielectric resonator antennas, metamaterials, reconfigurable intelligent surfaces, and meta-surfaces. Written by leading experts, the book offers insights into the modeling of device-to-device (D2D) networks, UAV-assisted communications, and RIS-assisted networks. Additionally, it explores antenna design and wireless communication networks using machine learning, artificial intelligence, deep learning, and optimization techniques for advanced communication networks, as well as the relevant security issues associated with such systems. This book serves as a valuable resource to researchers and graduate students working on next-generation advanced wireless communications.

Abhijit Bhowmick received his BE (hons) degree in Electronics and Telecommunication Engineering in 2002 from Burdwan University, West Bengal, India, and MTech degree in Telecommunication Engineering in 2009 and PhD in 2016 from NIT Durgapur. He joined VIT University, Vellore, India, in the School of Electronics Engineering in 2016 and is an Associate Professor there. His research interests include cognitive radio networks, focusing on spectrum sensing and spectrum sharing issues, cooperative communications in cognitive radio networks, energy harvesting in wireless network, D2D communication, physical layer security issues in wireless networks, and UAV-assisted communication. Yogesh Kumar Choukiker received his BE degree in 2007 and MTech and the PhD degrees from the National Institute of Technology, Rourkela, India, in 2009 and 2014, respectively, both in Electronics Engineering. He was a visiting research scholar at San Diego State University, California, USA, during his PhD, and has been a professor at Vellore Institute of Technology, since 2014. His specialties include antenna and wave propagation, microstrip antenna (fractal antennas) in the area of wireless applications, MIMO environments, UWB, and reconfigurable fractals. Saranya Karattupalayam Chidambaram is an Assistant Professor in the School of Electronics Engineering, VIT, India, where she has over thirteen years of experience in teaching and research. She received her B.Tech. degree in Electronics and Communication Engineering; an M.Tech. degree in Communication Engineering from VIT; an M.S. degree in Electrical Engineering and Information Technology from UAS, Darmstadt, Germany; and a Ph.D. in Investigation and Analysis of Vulnerable Road User Detection Methods for Intelligent Vehicle Navigation Systems from VIT. Her research interests include computer vision, deep learning, machine learning, signal processing, intelligent transportation systems and intelligent vehicles, discrete event systems, and active safety systems. Ananthakrishna Chintanpalli received his Master's degree in Electrical and Computer Engineering (with signal processing as a specialization) from the University of Rhode Island, Kingston, USA, in 2005 and a Ph.D. degree in Biomedical Engineering from Purdue University, West Lafayette, USA, in 2011. He then moved to the Medical University of South Carolina, Charleston, USA, and completed his Postdoctoral training in 2014. He is a Professor in the Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, India. His research interests include signal processing, biomedical signal processing, auditory modeling to predict speech recognition scores, psychoacoustics, speech processing, and deep neural networks. Justin Lee Bing received a B.E. (Hons.) degree and Ph.D. in Electronic and Communication Engineering from Curtin University, Australia in 2016 and 2020 respectively. He is working with Universiti Tunku Abdul Rahman (UTAR), Selangor, Malaysia as an Assistant Professor. His research interests include wireless communications, signal processing for communications, simultaneous wireless information and power transfer, stacked intelligence metasurfaces, and non-terrestrial networks.

1 Enabling Intelligent, Sustainable, and High-Performance Wireless Communication Systems. 2 Antenna Design for Wireless Power Transmission and Harvesting. 3 Design and Characterization of RFID Antennas and Systems. 4 Development of a Multilayer PCFSS-Based Checkerboard Metasurface for Wideband RCS Reduction. 5 Design and Analysis of a Metamaterial-Inspired Dual-Band Antenna for WiMax and WiFi Applications. 6 Optimized Implantable Antenna Design for Biomedical Devices in Modern Wireless Environments. 7 Dielectric Charging Phenomenon in Radio-Frequency Micro-Electro-Mechanical System Devices with Empirical Modelling. 8 Power Allocation and BER Optimization in NOMA Systems with CNN-Driven SWIPT. 9 Energy Harvesting in Cognitive Radio-Based IoT in 5G and Beyond: Role of Relays. 10 Reconfigurable Intelligent Surfaces and Unmanned Aerial Vehicle-Enabled Wireless Communication for Disaster Management Applications. 11 Analysis of UAV-Enabled RIS-Assisted Communication in Dust Atmospheric Environments. 12 Performance Optimization of IRS-Aided CR Networks. 13 RIS-Assisted Dual Network Coexistence: Secondary and Backscatter Transmission over Nakagami-m Fading Channels. 14 Convergence of Neuroscience and Wireless Communication: Spiking Neurons, A New Frontier. 15 Long-Range and High-Data-Rate Wireless Communications Using Orbital Angular Momentum Beams. 16 Optical Scattering Communications. 17 Intelligent Communication Systems: Machine Learning Perspectives. 18 Advancing Connected Vehicular Networks with Deep Learning and 5G-LPWAN: A Framework for Industry 5.0 Sustainable Transportation