The rapid evolution of 6G networks demands communication systems that go beyond data transmission toward intelligent understanding. Semantic communication (SemCom) represents a paradigm shift from transmitting bits to transmitting meaning. By integrating artificial intelligence, knowledge graphs, and reasoning capabilities, cognitive SemCom further enables networks to infer, interpret, and deliver only task-relevant information, thereby achieving higher efficiency, robustness, and explainability than classical SemCom architectures.
This book provides a comprehensive overview of cognitive SemCom, from fundamental theories to practical implementations. It explores how structured knowledge can be used for semantic encoding, reasoning, compression, and robust signal recovery under interference, model mismatch, and adversarial attacks. Core topics include UAV-enabled cognitive SemCom systems, multi-user and multi-modal resource allocation, and cross-layer semantic security. The book also demonstrates how semantic communication can support low-latency, energy-efficient, and intelligence-native 6G systems.
Designed for researchers, engineers, and graduate students in wireless communications, signal processing, and artificial intelligence, this book bridges communication theory with machine intelligence. Readers will gain a deep understanding of how cognitive abilities transform communication from symbol transmission to meaning exchange, and how these innovations enable future intelligent networks.
Table of Contents:
.- Chapter 1: Introduction to Cognitive Semantic Communication.- Chapter 2: Knowledge Graph-Driven Cognitive SemCom Systems.- Chapter 3: UAV Cognitive SemCom for Object Detection.- Chapter 4: Over-Air Cognitive SemCom.- Chapter 5: Robust Cognitive SemCom.-Chapter 6: Cognitive SemCom Against Semantic Impairment.-Chapter 7: Resource Allocation for Multi-Modal SemCom.-Chapter 8: Bit-Aware Resource Allocation for SemCom.-Chapter 9: Cross-Layer Secure SemCom.-Chapter 10: IRS-Enhanced Cross-Layer Secure SemCom.-Chapter 11: LLM-Driven Cognitive SemCom.
About the Author :
Wei Wu, IEEE Senior Member, received the Ph.D. degree in signal and information processing from the College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China, in 2017. He is currently a Professor with Nanjing University of Posts and Telecommunications.He has published more than 40 articles, including in IEEE Transactions on Communications, IEEE Systems Journal, IEEE Wireless Communications Letters, and IEEE GLOBECOM. His research interests include physical-layer security, UAV communications, energy-efficient resource allocation, edge computing, cognitive semantic communication, and intelligent spectrum management. He has served as a Technical Program Committee Member for several international conferences, such as IEEE GLOBECOM and IEEE ICC. He received the 2020 Exemplary Reviewer Award of IEEE Wireless Communications Letters. Since March 2021, he has been serving as a Guest Editor for the Special Issue on Green Edge/Fog/Cloud Computing of the Frontiers in Computer Science.
Fuhui Zhou, IEEE Senior Member, is currently a Full Professor with the Nanjing University of Aeronautics and Astronautics, China. His current research interests include cognitive radio, cognitive intelligence, cognitive semantic communication, knowledge graphs, edge computing, and resource allocation. He was awarded the IEEE ComSoc Asia-Pacific Outstanding Young Researcher, and Young Elite Scientist Award of China, and the URSI GASS Young Scientist. He is a Highly Cited Researcher and on the World's Top 2% Scientists list by Stanford University. He has published over 300 papers in internationally renowned journals and conferences in the field of communications. He has been selected for 2 ESI hot article and 15 ESI highly cited articles. He serves as an Editor for IEEE TCOM, IEEE SJ, and IEEE WCL.
Lingyi Wang received his BS in information and computing science from the College of Science, Nanjing University of Posts and Telecommunications, China in 2024. He is currently a direct PhD student at the Electrical and Computer Engineering Department at Virginia Tech. His research interests include wireless communications, semantic communications, machine learning, and resource allocation.
Yuhang Wu received the Ph. D degree in information and communication engineering from College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics in 2025. She is a Post-Doctoral Researcher with Nanjing University of Aeronautics and Astronautics. Her research interests include spectrum management, cognitive radio, mobile edge computing, intelligent reflecting surface, and wireless resource allocation.
Yihao Li received the B. Eng. degree in electronic information engineering from Nanjing University of Information Science and Technology in 2021 and the M. Eng. degree in information and communication engineering from the Nanjing University of Aeronautics and Astronautics in 2024. He is currently pursuing the Ph.D. degree in the National Mobile Communications Research Laboratory, Southeast University, Nanjing, China. His research interests include semantic communication, and wireless federated learning.
Han Hu, IEEE Member, received the M.S. and Ph.D. degrees from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2010 and 2013, respectively. From 2019 to 2020, she was a Visiting Scholar with the Department of Electrical and Computer Engineering, Utah State University, Logan, UT, USA. She is currently an Assistant Professor with the College of Communication and Information Engineering, Nanjing University of Posts and Telecommunications. Her current research interests include mobile edge computing, uninhabited aerial vehicle (UAV) communication, and semantic communication.
