{"product_id":"wireless-semantic-communications-isbn-9781394223305","title":"Wireless Semantic Communications","description":"\u003cp\u003e\u003cb\u003eUnderstand the cutting-edge technology of semantic communications and its growing applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eSemantic communications constitute a revolution in wireless technology, combining semantic theory with wireless communication. In a semantic communication, essential information is encoded at the source, drastically reducing the required data usage, and then decoded at the destination in such a way that all key information is recovered, even if transmission is damaged or incomplete. Enhancing the correspondence between background knowledge at source and destination can drive the data usage requirement even lower, producing ultra-efficient information exchanges with ultra-low semantic ambiguity. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eWireless Semantic Communications \u003c\/i\u003eoffers a comprehensive overview of this groundbreaking field, its development, and its future application. Beginning with an introduction to semantic communications and its foundational principles, the book then proceeds to cover transceiver design and methods, before discussing use cases and future developments. The result is an indispensable resource for understanding the future of wireless communication. \u003c\/p\u003e\u003cp\u003eReaders will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eAnalysis of transceiver optimization methods and resource management for semantic communication\u003c\/li\u003e\n\u003cli\u003eDetailed discussion of topics including semantic encoding and decoding, Shannon information theory, and many more\u003c\/li\u003e\n\u003cli\u003eA team of editors with decades of combined experience in the study of wireless communications\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eWireless Semantic Communications\u003c\/i\u003e is ideal for electrical and computing engineers and researchers, as well as industry professionals working in wireless communications. \u003c\/p\u003e\u003cp\u003eList of Contributions xiii\u003c\/p\u003e \u003cp\u003ePreface xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Intelligent Transceiver Design for Semantic Communication 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYiwen Wang, Yijie Mao, and Zhaohui Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Knowledge Base 1\u003c\/p\u003e \u003cp\u003e1.2 Source and Channel Coding 4\u003c\/p\u003e \u003cp\u003e1.3 Multiuser SC 7\u003c\/p\u003e \u003cp\u003e1.4 Transceiver Design for Single-Modal and Multimodal Data 13\u003c\/p\u003e \u003cp\u003e1.5 Challenges and Future Directions 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Joint Cell Association and Spectrum Allocation in Semantic Communication Networks 23\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLe Xia, Yao Sun, and Muhammad Ali Imran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 23\u003c\/p\u003e \u003cp\u003e2.2 Semantic Communication Model 26\u003c\/p\u003e \u003cp\u003e2.3 Optimal CA and SA Solution in the PKM-Based SC-Net 32\u003c\/p\u003e \u003cp\u003e2.4 Optimal CA and SA Solution in the IKM-Based SC-Net 35\u003c\/p\u003e \u003cp\u003e2.5 Numerical Results and Discussions 38\u003c\/p\u003e \u003cp\u003e2.6 Conclusions 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 An End-to-End Semantic Communication Framework for Image Transmission 47\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLei Feng, Yu Zhou, and Wenjing Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 47\u003c\/p\u003e \u003cp\u003e3.2 The End-to-End Image Semantic Communication Framework Driven by Knowledge Graph 50\u003c\/p\u003e \u003cp\u003e3.3 Semantic Similarity Measurement 59\u003c\/p\u003e \u003cp\u003e3.4 Simulation 62\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Robust Semantic Communications and Privacy Protection 67\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eXuefei Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Motivation and Introduction 67\u003c\/p\u003e \u003cp\u003e4.2 Robust Semantic Communication 68\u003c\/p\u003e \u003cp\u003e4.3 Knowledge Discrepancy-Oriented Privacy Protection for Semantic Communication 75\u003c\/p\u003e \u003cp\u003e4.4 Conclusion 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Interplay of Semantic Communication and Knowledge Learning 87\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eFei Ni, Bingyan Wang, Rongpeng Li, Zhifeng Zhao, and Honggang Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 87\u003c\/p\u003e \u003cp\u003e5.2 Basic Concepts and RelatedWorks 88\u003c\/p\u003e \u003cp\u003e5.3 A KG-enhanced SemCom System 91\u003c\/p\u003e \u003cp\u003e5.4 A KG Evolving-based SemCom System 99\u003c\/p\u003e \u003cp\u003e5.5 LLM-assisted Data Augmentation for the KG Evolving-Based SemCom System 104\u003c\/p\u003e \u003cp\u003e5.6 Conclusion 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 VISTA: A Semantic Communication Approach for Video Transmission 109\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChengsi Liang, Xiangyi Deng, Yao Sun, Runze Cheng, Le Xia, Dusit Niyato, and Muhammad Ali Imran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 109\u003c\/p\u003e \u003cp\u003e6.2 Video Transmission Framework in VISTA 110\u003c\/p\u003e \u003cp\u003e6.3 SLG-Based Transceiver Design in VISTA 111\u003c\/p\u003e \u003cp\u003e6.4 Simulation Results and Discussions 116\u003c\/p\u003e \u003cp\u003e6.5 Conclusions 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Content-Aware Robust Semantic Transmission of Images over Wireless Channels with GANs 123\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eXuyang Chen, Daquan Feng, Qi He, Yao Sun, and Xiang-Gen Xia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 123\u003c\/p\u003e \u003cp\u003e7.2 System Model 124\u003c\/p\u003e \u003cp\u003e7.3 System Architecture 127\u003c\/p\u003e \u003cp\u003e7.4 Experimental Results 127\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Semantic Communication in the Metaverse 133\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYijing Lin, Zhipeng Gao, Hongyang Du, Jiacheng Wang, and Jiakang Zheng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 133\u003c\/p\u003e \u003cp\u003e8.2 RelatedWork 134\u003c\/p\u003e \u003cp\u003e8.3 Unified Framework for SemCom in the Metaverse 137\u003c\/p\u003e \u003cp\u003e8.4 Zero-Knowledge Proof-Based Semantic Verification 142\u003c\/p\u003e \u003cp\u003e8.5 Diffusion Model-Based Resource Allocation 147\u003c\/p\u003e \u003cp\u003e8.6 Simulation Results 152\u003c\/p\u003e \u003cp\u003e8.7 Future Directions 155\u003c\/p\u003e \u003cp\u003e8.8 Conclusion 157\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Large Language Model-Assisted Semantic Communication Systems 163\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShuaishuai Guo, Yanhu Wang, Biqian Feng, and Chenyuan Feng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 163\u003c\/p\u003e \u003cp\u003e9.2 SSSC Using Pretrained LLMs 165\u003c\/p\u003e \u003cp\u003e9.3 SIAC Using Pretrained LLMs 171\u003c\/p\u003e \u003cp\u003e9.4 Future Direction of Using LLMs: Semantic Correction 178\u003c\/p\u003e \u003cp\u003e9.5 Conclusion 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 RIS-Enhanced Semantic Communication 183\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBohao Wang, Ruopeng Xu, Zhaohui Yang, and Chongwen Huang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 RIS-Empowered Communications 183\u003c\/p\u003e \u003cp\u003e10.2 Beamforming Design for RISs Enhanced Semantic Communications 184\u003c\/p\u003e \u003cp\u003e10.3 Privacy Protection in RIS-Assisted Semantic Communication System 189\u003c\/p\u003e \u003cp\u003e10.4 AI for RIS-Assisted Semantic Communications 191\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 195\u003c\/p\u003e \u003cp\u003eAcronyms 195\u003c\/p\u003e \u003cp\u003eReferences 196\u003c\/p\u003e \u003cp\u003eIndex 199\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eYao Sun, PhD,\u003c\/b\u003e is a Lecturer at the University of Glasgow, UK. His awards and honors include the IEEE Communication Society of TOAS Best Paper Award 2019 and the IEEE IoT Journal Best Paper Award 2022. He has served as a regular reviewer and editor for numerous international journals. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eLan Zhang, PhD,\u003c\/b\u003e is an Assistant Professor in the Electrical and Computer Engineering Department at Michigan Technological University, USA. She is also an Associate Editor of IEEE Transactions on Vehicular Technologies, and has published widely on machine learning, wireless communications, and related fields. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDusit Niyato, PhD,\u003c\/b\u003e is President’s Chair Professor in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He serves as Editor-in-Chief for IEEE Communications Surveys and Tutorials, as well as an Area Editor for IEEE Transactions on Vehicular Technology and an Editor for IEEE Transactions on Wireless Communications. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eMuhammad Ali Imran, PhD,\u003c\/b\u003e is a Professor in Communications Systems at the University of Glasgow, UK, Dean of Transnational Engineering Education and Dean of Graduate Studies in the College of Science and Engineering. He has published high impact articles on wireless communications and sensing subjects.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eUnderstand the cutting-edge technology of semantic communications and its growing applications\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eSemantic communications constitute a revolution in wireless technology, combining semantic theory with wireless communication. In a semantic communication, essential information is encoded at the source, drastically reducing the required data usage, and then decoded at the destination in such a way that all key information is recovered, even if transmission is damaged or incomplete. Enhancing the correspondence between background knowledge at source and destination can drive the data usage requirement even lower, producing ultra-efficient information exchanges with ultra-low semantic ambiguity. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eWireless Semantic Communications \u003c\/i\u003eoffers a comprehensive overview of this groundbreaking field, its development, and its future application. Beginning with an introduction to semantic communications and its foundational principles, the book then proceeds to cover transceiver design and methods, before discussing use cases and future developments. The result is an indispensable resource for understanding the future of wireless communication. \u003c\/p\u003e\u003cp\u003eReaders will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eAnalysis of transceiver optimization methods and resource management for semantic communication\u003c\/li\u003e\n\u003cli\u003eDetailed discussion of topics including semantic encoding and decoding, Shannon information theory, and many more\u003c\/li\u003e\n\u003cli\u003eA team of editors with decades of combined experience in the study of wireless communications\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eWireless Semantic Communications\u003c\/i\u003e is ideal for electrical and computing engineers and researchers, as well as industry professionals working in wireless communications.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47990498623717,"sku":"NP9781394223305","price":135.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394223305.jpg?v=1761788069","url":"https:\/\/k12savings.com\/products\/wireless-semantic-communications-isbn-9781394223305","provider":"K12savings","version":"1.0","type":"link"}