Intelligent Signal Processing and RF Energy Harvesting for State of art 5G and B5G Networks (eBook)

The book covers all the emerging paradigms of machine learning and bio-inspired algorithms and their synergies with communication networks which may prove to a core 5G and 6G enablers. It consists of 11 chapters with varied fields. The book introduces the fundamentals of broadband wireless networks and issues related to energy efficiency and optimization. Also, it discusses the efficient bio-inspired algorithms and their utility in wireless networks for 5G, B5G, and IoT. Different fitness functions for different bio-inspired and other artificial intelligence algorithms are described in the book. More importantly it also introduces the concept, implementation, and technological challenges of efficient wireless energy harvesting methods. The book discusses different methodologies for efficient antenna designs. It also covers real-time applications on the Internet of Medical Things (IOMT). The book helps the readers to understand the subject and solve many real-time issues. It proves a ready reference to the researchers working in RF, artificial intelligence, machine learning, and communication networks.

Javaid Ahmad Sheikh is Assistant Professor in the Department of Electronics and IT, University of Kashmir. He is Doctorate in Electronics from the University of Kashmir, Srinagar. His expertise in communications, RF, and signal processing is acknowledged by several organizations. He has supervised several students for their Ph.D., M. Phil., and M. Tech. degrees. He has guided more than hundred (100) technical projects having societal relevance. His interest areas are wireless communications, MIMO-based 5G and 6G communication systems, quantum machine learning for future networks, efficient antenna, filter design, and signal processing for communication systems.

Taimoor Khan is an Associate Professor in the Department of Electronics and Communication Engineering at the National Institute of Technology, Silchar, India. His active research interest areas are RF Energy Harvesting and Wireless Power Transfer, Microstrip Antennas, Electromagnetic Bandgap Structures, Dielectric Resonator Antennas, and Artificial Intelligence Paradigms in Electromagnetics. He has published over 140 research papers in international journals and conference proceedings. He is also involved in executing three sponsored research projects under the SPARC Scheme, VAJRA Scheme, and IMPRINT Scheme.

Binod Kumar Kanaujia is Professor in the School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India. Prof. Kanaujia's research interests include modeling for RF system design and microstrip antenna, dielectric resonator antenna, left-handed metamaterial microstrip antenna, shorted microstrip antenna, ultra-wideband antennas, reconfigurable and circular polarized antenna for wireless communication and mobile communication, and computational RF radiator. Dr. Kanaujia has guided several students for their Ph. D. program and published over 150 papers in reputed journals.

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The book covers all the emerging paradigms of machine learning and bio-inspired algorithms and their synergies with communication networks which may prove to a core 5G and 6G enablers. It consists of 11 chapters with varied fields. The book introduces the fundamentals of broadband wireless networks and issues related to energy efficiency and optimization. Also, it discusses the efficient bio-inspired algorithms and their utility in wireless networks for 5G, B5G, and IoT. Different fitness functions for different bio-inspired and other artificial intelligence algorithms are described in the book. More importantly it also introduces the concept, implementation, and technological challenges of efficient wireless energy harvesting methods. The book discusses different methodologies for efficient antenna designs. It also covers real-time applications on the Internet of Medical Things (IOMT). The book helps the readers to understand the subject and solve many real-time issues. It proves a ready reference to the researchers working in RF, artificial intelligence, machine learning, and communication networks.