{"product_id":"generative-ai-for-communications-systems-isbn-9781394293902","title":"Generative AI for Communications Systems","description":"\u003cp\u003e\u003cb\u003eComprehensive review of state-of-the-art research and development in Generative AI for future communications and networking\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eGenerative AI for Communications Systems\u003c\/i\u003e provides a systematic foundation of knowledge on Generative AI for communications and networking. This book discusses the great potential and challenges in applying Generative AI as promising solutions to future communications systems and enables and facilitates “Generative AI as a Service” by exploring novel communications, networking architectures, protocols, and research trends. \u003c\/p\u003e\u003cp\u003eThe book also includes information on: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eCrucial challenges to solve in Generative AI, such as training data availability, computational complexity, generalization for various scenarios, robustness of noisy and incomplete data, and real-time adaptation in communications and networking systems\u003c\/li\u003e \u003cli\u003eCybersecurity concerns such as ethics and privacy in relation to Generative AI\u003c\/li\u003e \u003cli\u003eApplications of Generative AI across various layers, including the PHY layer, MAC layer, Network layer, and Application layer\u003c\/li\u003e \u003cli\u003eCommunications and networking solutions to meet the computing and communications challenges and demands to train and deploy large-scale Generative AI models\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eGenerative AI for Communications Systems\u003c\/i\u003e is an excellent up-to-date resource on the subject for scholars and researchers in the fields of communications, artificial intelligence, machine learning, and network optimization as well as professionals working in the communications industry including engineers, network architects, and system designers. \u003c\/p\u003e\u003cp\u003eContributors\u003c\/p\u003e \u003cp\u003eForeword\u003c\/p\u003e \u003cp\u003ePreface\u003c\/p\u003e \u003cp\u003eAcknowledgments\u003c\/p\u003e \u003cp\u003eAcronyms\u003c\/p\u003e \u003cp\u003eIntroduction\u003c\/p\u003e \u003cp\u003e1 Future AI-empowered Communications Systems\u003c\/p\u003e \u003cp\u003e1.1 Fundamental Background of Future Communications Systems\u003c\/p\u003e \u003cp\u003e1.1.1 Overview of Future Communications Systems\u003c\/p\u003e \u003cp\u003e1.1.2 Key Challenges and Research Trends\u003c\/p\u003e \u003cp\u003e1.2 AI-powered Communication Enablers\u003c\/p\u003e \u003cp\u003e1.2.1 Deep Learning-based Approaches\u003c\/p\u003e \u003cp\u003e1.2.2 Reinforcement Learning-based Approaches\u003c\/p\u003e \u003cp\u003e1.2.3 Federated\/Distributed Learning-based Approaches\u003c\/p\u003e \u003cp\u003e1.2.4 Existing Challenges\u003c\/p\u003e \u003cp\u003e1.2.5 Potential of Generative AI\u003c\/p\u003e \u003cp\u003e1.3 Conclusion\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003e2 Generative AI Background and Its Potentials for Future Communications Systems\u003c\/p\u003e \u003cp\u003e2.1 Introduction\u003c\/p\u003e \u003cp\u003e2.2 A Taxonomy of Generative Models\u003c\/p\u003e \u003cp\u003e2.2.1 Explicit Density Models\u003c\/p\u003e \u003cp\u003e2.2.2 Implicit Density Models\u003c\/p\u003e \u003cp\u003e2.2.3 Ways GenAI complements DAI\u003c\/p\u003e \u003cp\u003e2.3 Prominent Generative Models\u003c\/p\u003e \u003cp\u003e2.3.1 Generative Adversarial Networks\u003c\/p\u003e \u003cp\u003e2.3.2 Variational Autoencoders\u003c\/p\u003e \u003cp\u003e2.3.3 Flow-based Generative Models\u003c\/p\u003e \u003cp\u003e2.3.4 Diffusion-based Generative Models\u003c\/p\u003e \u003cp\u003e2.3.5 The Trilemma of GMs\u003c\/p\u003e \u003cp\u003e2.3.6 Generative Autoregressive Models\u003c\/p\u003e \u003cp\u003e2.3.7 Generative Transformers and LLMs\u003c\/p\u003e \u003cp\u003e2.3.8 Strategies to Address LLM Limitations\u003c\/p\u003e \u003cp\u003e2.4 GenAI Applications to Canonical Problems in Communications Systems\u003c\/p\u003e \u003cp\u003e2.4.1 Physical Layer Design\u003c\/p\u003e \u003cp\u003e2.4.2 Network Resource Management\u003c\/p\u003e \u003cp\u003e2.4.3 Network Traffic Analytics\u003c\/p\u003e \u003cp\u003e2.4.4 Cross-Layer Network Security\u003c\/p\u003e \u003cp\u003e2.4.5 Localization and Positioning\u003c\/p\u003e \u003cp\u003e2.5 Future Communication Frontiers for Generative Models\u003c\/p\u003e \u003cp\u003e2.5.1 Semantic Communications\u003c\/p\u003e \u003cp\u003e2.5.2 Integrated Sensing and Communications\u003c\/p\u003e \u003cp\u003e2.5.3 Digital Twins\u003c\/p\u003e \u003cp\u003e2.5.4 AI-Generated Content for 6G Networks\u003c\/p\u003e \u003cp\u003e2.5.5 Mobile Edge Computing and Edge AI\u003c\/p\u003e \u003cp\u003e2.5.6 Adversarial Machine Learning and Trustworthy AI\u003c\/p\u003e \u003cp\u003e2.6 Regulation and Policy\u003c\/p\u003e \u003cp\u003e2.7 Summary\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003e3 Key Study Cases of Generative AI Applications to Communications Systems\u003c\/p\u003e \u003cp\u003e3.1 Overview on The Roles of Generative AI in Communication Systems\u003c\/p\u003e \u003cp\u003e3.1.1 Use-Cases of Generative Adversarial Networks in Communications\u003c\/p\u003e \u003cp\u003e3.1.2 Use-Cases of Variational Autoencoders in Communications\u003c\/p\u003e \u003cp\u003e3.1.3 Use-Cases of Diffusion Models in Communications\u003c\/p\u003e \u003cp\u003e3.2 Case Study: Diffusion Models in Wireless Communications\u003c\/p\u003e \u003cp\u003e3.2.1 Working Mechanism of Diffusion Models\u003c\/p\u003e \u003cp\u003e3.2.2 Case Study: Diffusion Models Applications for Data Reconstruction Enhancement in Communication Systems\u003c\/p\u003e \u003cp\u003e3.3 Future Implications \u0026amp; Potential Impacts on Communication Systems\u003c\/p\u003e \u003cp\u003e3.3.1 Chapter Summary\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003e4 Generative AI at PHY Layer: Native AI or Trainable Radios\u003c\/p\u003e \u003cp\u003e4.1 Wireless Communications Empowered with Generative Models\u003c\/p\u003e \u003cp\u003e4.1.1 Motivations of GenAI at the PHY\u003c\/p\u003e \u003cp\u003e4.1.2 Applications of GenAI at the PHY\u003c\/p\u003e \u003cp\u003e4.2 Channel Modeling\u003c\/p\u003e \u003cp\u003e4.2.1 Generative Channel Modeling\u003c\/p\u003e \u003cp\u003e4.2.2 Site-Specific Generative Models\u003c\/p\u003e \u003cp\u003e4.3 Generative Channel Estimation\u003c\/p\u003e \u003cp\u003e4.3.1 Narrowband Channel Estimation with Reduced Pilots\u003c\/p\u003e \u003cp\u003e4.3.2 Wideband Channel Estimation with Reduced Pilots\u003c\/p\u003e \u003cp\u003e4.4 Channel Compression\u003c\/p\u003e \u003cp\u003e4.5 Beamforming\u003c\/p\u003e \u003cp\u003e4.6 Summary\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003e5 Generative AI at the MAC Layer\u003c\/p\u003e \u003cp\u003e5.1 Introduction\u003c\/p\u003e \u003cp\u003e5.2 Generative Models\u003c\/p\u003e \u003cp\u003e5.2.1 Variational Autoencoders\u003c\/p\u003e \u003cp\u003e5.2.2 Generative Adversarial Networks\u003c\/p\u003e \u003cp\u003e5.2.3 Diffusion Models\u003c\/p\u003e \u003cp\u003e5.3 Spectrum Awareness Applications\u003c\/p\u003e \u003cp\u003e5.3.1 Data Augmentation and Synthetic Data Generation\u003c\/p\u003e \u003cp\u003e5.3.2 Signal Classification Applications - UAV Classification\u003c\/p\u003e \u003cp\u003e5.3.3 Anomaly Detection in RF Spectrum\u003c\/p\u003e \u003cp\u003e5.4 RF Spectrum Security Applications\u003c\/p\u003e \u003cp\u003e5.4.1 Emitter Identification\u003c\/p\u003e \u003cp\u003e5.4.2 Wireless Spoofing\u003c\/p\u003e \u003cp\u003e5.4.3 Enhanced Jamming Attacks\u003c\/p\u003e \u003cp\u003e5.5 Scheduling Applications\u003c\/p\u003e \u003cp\u003e5.5.1 Traffic Prediction and Pattern Generation\u003c\/p\u003e \u003cp\u003e5.5.2 Adaptive Scheduling Algorithms\u003c\/p\u003e \u003cp\u003e5.5.3 Interference Patterns\u003c\/p\u003e \u003cp\u003e5.5.4 Fairness and QoS\u003c\/p\u003e \u003cp\u003e5.5.5 Millimeter-Wave Networks\u003c\/p\u003e \u003cp\u003e5.6 Open Problems and Future Research Directions\u003c\/p\u003e \u003cp\u003e5.6.1 Reconfigurable Intelligent Surface (RIS)-Assisted Networks\u003c\/p\u003e \u003cp\u003e5.6.2 Spectrum Sharing in the Presence of Interference\u003c\/p\u003e \u003cp\u003e5.6.3 Integrated Sensing and Communications (ISAC)\u003c\/p\u003e \u003cp\u003e5.6.4 Link Scheduling in Large Networks\u003c\/p\u003e \u003cp\u003e5.6.5 Enhancing Wireless MAC-Layer Security\u003c\/p\u003e \u003cp\u003e5.7 Concluding Remarks\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003e6 Generative AI at Network Layer\u003c\/p\u003e \u003cp\u003e6.1 Introduction\u003c\/p\u003e \u003cp\u003e6.2 Network Layer in Mobile Networks\u003c\/p\u003e \u003cp\u003e6.2.1 RAN\u003c\/p\u003e \u003cp\u003e6.2.2 Core Network\u003c\/p\u003e \u003cp\u003e6.3 Generative AI in the Network Layer\u003c\/p\u003e \u003cp\u003e6.3.1 Introduction\u003c\/p\u003e \u003cp\u003e6.3.2 Advantages of GenAI models\u003c\/p\u003e \u003cp\u003e6.3.3 Short-term applications (GenAI for Network Layer)\u003c\/p\u003e \u003cp\u003e6.3.4 Long-term applications (Network Layer for GenAI)\u003c\/p\u003e \u003cp\u003e6.4 Challenges and Opportunities for GenAI in the Network Layer\u003c\/p\u003e \u003cp\u003e6.4.1 Challenges\u003c\/p\u003e \u003cp\u003e6.4.2 Research Opportunities\u003c\/p\u003e \u003cp\u003e6.5 Chapter Summary\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003e7 Generative AI at Application Layer: Mobile AI-Generated Content\u003c\/p\u003e \u003cp\u003e7.1 Introduction to AIGC\u003c\/p\u003e \u003cp\u003e7.1.1 General Overview\u003c\/p\u003e \u003cp\u003e7.1.2 AIGC in the Application Layer\u003c\/p\u003e \u003cp\u003e7.1.3 AIGC Product Lifecycle\u003c\/p\u003e \u003cp\u003e7.2 Collaborative Network Infrastructure for Enabling GenAI Services\u003c\/p\u003e \u003cp\u003e7.2.1 Enabling AIGC - Challenges\u003c\/p\u003e \u003cp\u003e7.2.2 Infrastructure Components and Capabilities\u003c\/p\u003e \u003cp\u003e7.2.3 Collaborative Edge-Cloud Infrastructure\u003c\/p\u003e \u003cp\u003e7.3 Network Resource Efficient GenAI Methods\u003c\/p\u003e \u003cp\u003e7.3.1 Model Optimization Techniques\u003c\/p\u003e \u003cp\u003e7.3.2 Service Optimization Methods\u003c\/p\u003e \u003cp\u003e7.4 Security and Privacy at Application Layer\u003c\/p\u003e \u003cp\u003e7.4.1 Security Threat Models and Privacy Risks\u003c\/p\u003e \u003cp\u003e7.4.2 Ethical Considerations in AIGC services\u003c\/p\u003e \u003cp\u003e7.4.3 Enabling Secure AIGC-as-a-Service\u003c\/p\u003e \u003cp\u003e7.5 Use Cases of Mobile AIGC\u003c\/p\u003e \u003cp\u003e7.5.1 AI-Generated Content in Social Media\u003c\/p\u003e \u003cp\u003e7.5.2 Immersive Streaming (AR\/VR)\u003c\/p\u003e \u003cp\u003e7.5.3 Personalized AI Services\u003c\/p\u003e \u003cp\u003e7.6 Conclusion and Research Directions\u003c\/p\u003e \u003cp\u003e7.7 Summary\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003e8 Applications of GenAI on Wireless and Cybersecurity\u003c\/p\u003e \u003cp\u003e8.1 Introduction to GenAI in Wireless and Cybersecurity\u003c\/p\u003e \u003cp\u003e8.2 Adversarial machine learning in wireless communications\u003c\/p\u003e \u003cp\u003e8.2.1 Different types of attacks against GenAI-driven wireless applications\u003c\/p\u003e \u003cp\u003e8.2.2 Defense against adversarial attacks for GenAI-driven wireless applications\u003c\/p\u003e \u003cp\u003e 8.3 GenAI for wireless security and cybersecurity\u003c\/p\u003e \u003cp\u003e8.3.1 GenAI for wireless security\u003c\/p\u003e \u003cp\u003e8.3.2 GenAI for cybersecurity\u003c\/p\u003e \u003cp\u003e8.3.3 GenAI-driven attacks against wireless and cybersecurity applications\u003c\/p\u003e \u003cp\u003e8.4 Ethical issues related to GenAI for wireless communications and cybersecurity\u003c\/p\u003e \u003cp\u003e8.5 Summary\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003e9 Challenges and Opportunities for Generative AI in Wireless\u003c\/p\u003e \u003cp\u003eCommunications and Networking\u003c\/p\u003e \u003cp\u003e9.1 Introduction\u003c\/p\u003e \u003cp\u003e9.2 Challenges of Applying Generative AI in Wireless Communications\u003c\/p\u003e \u003cp\u003e9.2.1 Efficiency and Robustness\u003c\/p\u003e \u003cp\u003e9.2.2 Cost and Complexity\u003c\/p\u003e \u003cp\u003e9.2.3 Standardization, Regulation, and Policy\u003c\/p\u003e \u003cp\u003e9.3 Adopting Generative AI in NextG Communications: Case Studies\u003c\/p\u003e \u003cp\u003e9.3.1 Integration of Generative AI and Physical Communications Models\u003c\/p\u003e \u003cp\u003e9.3.2 Trustworthy Generative AI for Distributed Wireless Communications\u003c\/p\u003e \u003cp\u003e9.4 Summary\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e \u003cp\u003e10 Future Research Directions\u003c\/p\u003e \u003cp\u003e10.1 Introduction\u003c\/p\u003e \u003cp\u003e10.2 Emerging Foundational Research Frontiers\u003c\/p\u003e \u003cp\u003e10.2.1 Dedicated GenAI models for communication systems\u003c\/p\u003e \u003cp\u003e10.2.2 Fusion of GenAI and Emerging Technologies\u003c\/p\u003e \u003cp\u003e10.3 Enhancing Generative AI Models for Wireless Communication Systems\u003c\/p\u003e \u003cp\u003e10.3.1 Model Optimization and Generalization\u003c\/p\u003e \u003cp\u003e10.3.2 Energy Efficiency\u003c\/p\u003e \u003cp\u003e10.3.3 Generative AI for Spectrum Management\u003c\/p\u003e \u003cp\u003e10.3.4 AI-driven Network Management and Orchestration\u003c\/p\u003e \u003cp\u003e10.3.5 Security and Privacy Concerns\u003c\/p\u003e \u003cp\u003e10.4 Practical Case Studies\u003c\/p\u003e \u003cp\u003e10.4.1 AI-Powered Network Optimization by T-Mobile\u003c\/p\u003e \u003cp\u003e10.4.2 DeepSig’s Generative AI for Wireless Communications\u003c\/p\u003e \u003cp\u003e10.5 Conclusion\u003c\/p\u003e \u003cp\u003eBibliography\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eDiep N. Nguyen\u003c\/b\u003e is the Head of UTS 5G\/6G Lab with the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), Sydney, NSW, Australia. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eNam H. Chu\u003c\/b\u003e is a Faculty Member with the Department of Telecommunications Engineering at the University of Transport and Communications, Hanoi, Vietnam. He is also with the University of Technology Sydney (UTS), Australia, and the Crown Institute of Higher Education, Australia. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDinh Thai Hoang\u003c\/b\u003e is a Faculty Member at the University of Technology Sydney (UTS), Australia. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eOctavia A. Dobre\u003c\/b\u003e is a Professor and Canada Research Chair Tier-1 at Memorial University, Canada. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDusit Niyato\u003c\/b\u003e is a President's Chair Professor in Computer Science and Engineering in the College of Computing and Data Science at Nanyang Technological University, Singapore. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePetar Popovski\u003c\/b\u003e is currently a Professor with Aalborg University in Denmark where he heads the Section on Connectivity. He is also a Visiting Excellence Chair with the University of Bremen, Germany.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989277425893,"sku":"NP9781394293902","price":150.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394293902.jpg?v=1761783487","url":"https:\/\/k12savings.com\/products\/generative-ai-for-communications-systems-isbn-9781394293902","provider":"K12savings","version":"1.0","type":"link"}