Next-Gen Electronics and Communication Systems

Pankaj Verma received the AMIETE (Associate Member of the Institution of Electronics and Telecommunication Engineers) degree from the Institution of Electronics and Telecommunication Engineers (IETE), New Delhi, India, in 2009, the M.Tech. degree from Delhi Technological University (formerly Delhi College of Engineering), Delhi, in 2011, and the Ph.D. degree from the National Institute of Technology (NIT), Kurukshetra, India, in 2017. He is currently working as an Assistant Professor in the Department of Electronics and Communication Engineering at the National Institute of Technology, Kurukshetra, India. He was awarded the Gold Medal in the AMIETE examination in 2009 for outstanding academic performance. He has published approximately 50+ research papers including abroad paper presentations including Australia, Mauritius and UK. He has organized a number of international conferences, Faculty Development Programs (FDPs), workshops, and technical events. Before joining academia, he gained one year of industrial experience in the Intellectual Property (IP) domain and served in the Indian Air Force for three years. His research interests include wireless communications, cognitive radio networks, spectrum sharing, optical communications, and optical sensors. His current research interests include the applications of Artificial Intelligence (AI) techniques to wireless communication systems and other allied research areas.Chhagan Charan is an Assistant Professor in the Department of Electronics and Communication Engineering at the National Institute of Technology (NIT) Kurukshetra, India. He received his B.Tech. in Electronics and Communication Engineering and M.Tech. in Microwave and Optical Communication Engineering from Delhi Technological University (DTU), Delhi, and completed his Ph.D. from the National Institute of Technology (NIT) Kurukshetra in 2018. Dr. Charan has over 14 years of teaching and research experience in the field of Electronics and Communication Engineering. His research interests include Cognitive Radio, Digital Communication, Microwave Engineering, Wireless Sensor Networks, Artificial Intelligence (AI) and Machine Learning (ML) for Next-Generation Wireless Communications, and Next-Generation Communication Systems. His work focuses on spectrum-efficient wireless communication, intelligent communication networks, and AI-enabled solutions for future wireless communication systems. He has published more than 30 research papers in reputed national and international journals and conferences. His research has been presented at international conferences in Norway and Greece, reflecting his active engagement with the global research community. He is actively involved in teaching, research, and academic activities, contributing to the advancement of electronics and communication engineering through quality research and innovation.T. N. Sasamal is currently serving as an Assistant Professor in the Department of Electronics and Communication Engineering at the National Institute of Technology (NIT), Kurukshetra, Haryana, India, where he has been a faculty member since August 2013. He has over 14 years of teaching and research experience in the field of Electronics and Communication Engineering. Dr. Sasamal obtained his Ph.D. in Electronics and Communication Engineering from NIT Kurukshetra, Haryana. He received his M.Tech. in Electronics Engineering from the Indian Institute of Technology (Banaras Hindu University), Varanasi, India, and earned his B.Tech. in Electronics and Telecommunication Engineering from KEC, Bhubaneswar, India, in 2007. He has authored and co-authored more than 60 research papers in reputed international journals and conference proceedings. His research interests include Embedded System Design, FPGA-Based Digital Design, Quantum-dot Cellular Automata (QCA), Reversible Logic, and emerging nanoelectronic computing architectures. Dr. Sasamal is the author of the book Quantum-Dot Cellular Automata Based Digital Logic Circuits: A Design Perspective. He has also edited the book Quantum-Dot Cellular Automata Circuits for Nanocomputing Applications, published by CRC Press, Taylor & Francis Group.Mohit Dua received Ph.D. in Automatic Speech Recognition from National Institute of Technology, Kurukshetra, India in 2018. He is presently working as Assistant Professor in the Department of Computer Engineering at NIT Kurukshetra, India. He has more than 21 years of Teaching and Research experience. He is a Senior member of Institute of Electrical and Electronics Engineers (IEEE), and a life member of the Computer Society of India (CSI), ACM and Indian Society for Technical Education (ISTE). His research interests include Speech processing, Chaos based Cryptography, Information Security, Theory of Formal languages, Statistical modelling and Natural Language Processing. He has published approximately 160+ research papers including abroad paper presentations including USA, UK, Canada, Australia, Singapore, Mauritius and Dubai.

• Chapter 1. Artificial Intelligence for Spectrum Sensing in Cognitive Radio Networks. 1.1. Introduction. 1.2. Cognitive Radio Functions. 1.3. Mathematical Model of Spectrum Sensing. 1.4. Conventional Spectrum Sensing Techniques. 1.5. Limitations of Traditional Spectrum Sensing Methods. 1.6. Deep Learning-Based Spectrum Sensing. 1.7. AI-based Cooperative Spectrum Sensing. 1.8. Wideband Spectrum Sensing with AI. 1.9. Challenges and Future Research Directions. 1.10. Future Trends. 1.11. Conclusion. Chapter 2. Technique, Challenges and applications of integrating Machine Learning with PCF-SPR Sensors. 2.1. Introduction. 2.2. Fundamentals of PCF-SPR Sensors. 2.3. Machine Learning Approaches. 2.4. Trends and Future Directions. 2.5. Challenges of ML-based PCF-SPR Sensors. 2.6. Comparative Analysis of ML Algorithms in PCF-SPR Sensors. 2.7. Emerging Applications of ML-Assisted PCF-SPR Sensors. 2.8. Future Research Directions. 2.9. Future Outlook. 2.10. Conclusion. Chapter 3. AI-Driven Automatic Modulation Classification in Modern Wireless Communications. 3.1. Introduction. 3.2. Fundamentals of Wireless Communication and Digital Modulation. 3.3. Signal Representation and Feature Extraction Techniques. 3.4. Public Datasets for Automatic Modulation Classification Research. 3.5. Traditional Automatic Modulation Classification Methods. 3.6. Deep Learning-Based Automatic Modulation Classification. 3.7. Applications of Automatic Modulation Classification. 3.8. Challenges in Automatic Modulation Classification. 3.9. Future Research Directions. 3.10. Conclusion. Chapter 4. AI-Driven WiFi Sensing Technologies. 4.1. Sensing and Monitoring Systems. 4.2. Evolution of WiFi Sensing. 4.3. Wireless Signal Propagation and Multipath Effects. 4.4. WiFi Communication Technologies. 4.5. Device-Based and Device-Free WiFi Sensing. 4.6. WiFi Sensing System Architecture. 4.7. Public Datasets for WiFi Sensing Research. 4.8. Traditional WiFi Sensing Approaches. 4.9. Machine Learning and Deep Learning-Based WiFi Sensing. 4.10. Signal Processing Techniques in WiFi Sensing. 4.11. Applications of WiFi Sensing. 4.12. Challenges in WiFi Sensing. 4.13. Future Research Directions. Chapter 5. AI-Assisted Antenna Design and Optimization for Next-Generation Wireless Systems. 5.1. Foundations of AI-Assisted Antenna Design. 5.2. Machine Learning in Antenna Design: Concepts and Algorithms. 5.3. Machine Learning–Based Antenna Design and Optimization. 5.4. Physics-Aware and Generative Learning for Electromagnetic Design. 5.5. ML/DL-Based Antenna Optimization and Selection. 5.6. Challenges and Future Directions. Chapter 6. Artificial Intelligence for Integrated Sensing and Communication System. 6.1. Evolution of Intelligent ISAC Architectures for 6G. 6.2. Fundamentals Of Integrated Sensing and Communication (ISAC). 6.3. System Model and Problem Formulation. 6.4. AI-Based ISAC Design and Optimization. 6.5. Communication–Sensing Trade-Off and Computational Complexity. 6.6. Robustness and Practical Consideration. 6.7. Conclusion and Future Research Directions. Chapter 7. From Vectors to Vertices: A Review of AI Paradigms for Fault Detection in Modern VLSI Circuits. 7.1. Introduction. 7.2. Foundations of VLSI Fault Detection. 7.3. Early AI Approaches: Topology- Independent Methods. 7.4. Flattened Features to Graphs. 7.5. Training, Metrics and Evaluation. 7.6. Open Challenges and Research Directions. 7.7. A Small Example: Workflow for a Diagnostic Tool. 7.8. Limitations. 7.9. Conclusion.