Generative AI for Communications Systems

Comprehensive review of state-of-the-art research and development in Generative AI for future communications and networking Generative AI for Communications Systems 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. The book also includes information on: Crucial 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 Cybersecurity concerns such as ethics and privacy in relation to Generative AI Applications of Generative AI across various layers, including the PHY layer, MAC layer, Network layer, and Application layer Communications and networking solutions to meet the computing and communications challenges and demands to train and deploy large-scale Generative AI models Generative AI for Communications Systems 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.

Table of Contents:
Contributors Foreword Preface Acknowledgments Acronyms Introduction 1 Future AI-empowered Communications Systems 1.1 Fundamental Background of Future Communications Systems 1.1.1 Overview of Future Communications Systems 1.1.2 Key Challenges and Research Trends 1.2 AI-powered Communication Enablers 1.2.1 Deep Learning-based Approaches 1.2.2 Reinforcement Learning-based Approaches 1.2.3 Federated/Distributed Learning-based Approaches 1.2.4 Existing Challenges 1.2.5 Potential of Generative AI 1.3 Conclusion Bibliography 2 Generative AI Background and Its Potentials for Future Communications Systems 2.1 Introduction 2.2 A Taxonomy of Generative Models 2.2.1 Explicit Density Models 2.2.2 Implicit Density Models 2.2.3 Ways GenAI complements DAI 2.3 Prominent Generative Models 2.3.1 Generative Adversarial Networks 2.3.2 Variational Autoencoders 2.3.3 Flow-based Generative Models 2.3.4 Diffusion-based Generative Models 2.3.5 The Trilemma of GMs 2.3.6 Generative Autoregressive Models 2.3.7 Generative Transformers and LLMs 2.3.8 Strategies to Address LLM Limitations 2.4 GenAI Applications to Canonical Problems in Communications Systems 2.4.1 Physical Layer Design 2.4.2 Network Resource Management 2.4.3 Network Traffic Analytics 2.4.4 Cross-Layer Network Security 2.4.5 Localization and Positioning 2.5 Future Communication Frontiers for Generative Models 2.5.1 Semantic Communications 2.5.2 Integrated Sensing and Communications 2.5.3 Digital Twins 2.5.4 AI-Generated Content for 6G Networks 2.5.5 Mobile Edge Computing and Edge AI 2.5.6 Adversarial Machine Learning and Trustworthy AI 2.6 Regulation and Policy 2.7 Summary Bibliography 3 Key Study Cases of Generative AI Applications to Communications Systems 3.1 Overview on The Roles of Generative AI in Communication Systems 3.1.1 Use-Cases of Generative Adversarial Networks in Communications 3.1.2 Use-Cases of Variational Autoencoders in Communications 3.1.3 Use-Cases of Diffusion Models in Communications 3.2 Case Study: Diffusion Models in Wireless Communications 3.2.1 Working Mechanism of Diffusion Models 3.2.2 Case Study: Diffusion Models Applications for Data Reconstruction Enhancement in Communication Systems 3.3 Future Implications & Potential Impacts on Communication Systems 3.3.1 Chapter Summary Bibliography 4 Generative AI at PHY Layer: Native AI or Trainable Radios 4.1 Wireless Communications Empowered with Generative Models 4.1.1 Motivations of GenAI at the PHY 4.1.2 Applications of GenAI at the PHY 4.2 Channel Modeling 4.2.1 Generative Channel Modeling 4.2.2 Site-Specific Generative Models 4.3 Generative Channel Estimation 4.3.1 Narrowband Channel Estimation with Reduced Pilots 4.3.2 Wideband Channel Estimation with Reduced Pilots 4.4 Channel Compression 4.5 Beamforming 4.6 Summary Bibliography 5 Generative AI at the MAC Layer 5.1 Introduction 5.2 Generative Models 5.2.1 Variational Autoencoders 5.2.2 Generative Adversarial Networks 5.2.3 Diffusion Models 5.3 Spectrum Awareness Applications 5.3.1 Data Augmentation and Synthetic Data Generation 5.3.2 Signal Classification Applications - UAV Classification 5.3.3 Anomaly Detection in RF Spectrum 5.4 RF Spectrum Security Applications 5.4.1 Emitter Identification 5.4.2 Wireless Spoofing 5.4.3 Enhanced Jamming Attacks 5.5 Scheduling Applications 5.5.1 Traffic Prediction and Pattern Generation 5.5.2 Adaptive Scheduling Algorithms 5.5.3 Interference Patterns 5.5.4 Fairness and QoS 5.5.5 Millimeter-Wave Networks 5.6 Open Problems and Future Research Directions 5.6.1 Reconfigurable Intelligent Surface (RIS)-Assisted Networks 5.6.2 Spectrum Sharing in the Presence of Interference 5.6.3 Integrated Sensing and Communications (ISAC) 5.6.4 Link Scheduling in Large Networks 5.6.5 Enhancing Wireless MAC-Layer Security 5.7 Concluding Remarks Bibliography 6 Generative AI at Network Layer 6.1 Introduction 6.2 Network Layer in Mobile Networks 6.2.1 RAN 6.2.2 Core Network 6.3 Generative AI in the Network Layer 6.3.1 Introduction 6.3.2 Advantages of GenAI models 6.3.3 Short-term applications (GenAI for Network Layer) 6.3.4 Long-term applications (Network Layer for GenAI) 6.4 Challenges and Opportunities for GenAI in the Network Layer 6.4.1 Challenges 6.4.2 Research Opportunities 6.5 Chapter Summary Bibliography 7 Generative AI at Application Layer: Mobile AI-Generated Content 7.1 Introduction to AIGC 7.1.1 General Overview 7.1.2 AIGC in the Application Layer 7.1.3 AIGC Product Lifecycle 7.2 Collaborative Network Infrastructure for Enabling GenAI Services 7.2.1 Enabling AIGC - Challenges 7.2.2 Infrastructure Components and Capabilities 7.2.3 Collaborative Edge-Cloud Infrastructure 7.3 Network Resource Efficient GenAI Methods 7.3.1 Model Optimization Techniques 7.3.2 Service Optimization Methods 7.4 Security and Privacy at Application Layer 7.4.1 Security Threat Models and Privacy Risks 7.4.2 Ethical Considerations in AIGC services 7.4.3 Enabling Secure AIGC-as-a-Service 7.5 Use Cases of Mobile AIGC 7.5.1 AI-Generated Content in Social Media 7.5.2 Immersive Streaming (AR/VR) 7.5.3 Personalized AI Services 7.6 Conclusion and Research Directions 7.7 Summary Bibliography 8 Applications of GenAI on Wireless and Cybersecurity 8.1 Introduction to GenAI in Wireless and Cybersecurity 8.2 Adversarial machine learning in wireless communications 8.2.1 Different types of attacks against GenAI-driven wireless applications 8.2.2 Defense against adversarial attacks for GenAI-driven wireless applications  8.3 GenAI for wireless security and cybersecurity 8.3.1 GenAI for wireless security 8.3.2 GenAI for cybersecurity 8.3.3 GenAI-driven attacks against wireless and cybersecurity applications 8.4 Ethical issues related to GenAI for wireless communications and cybersecurity 8.5 Summary Bibliography 9 Challenges and Opportunities for Generative AI in Wireless Communications and Networking 9.1 Introduction 9.2 Challenges of Applying Generative AI in Wireless Communications 9.2.1 Efficiency and Robustness 9.2.2 Cost and Complexity 9.2.3 Standardization, Regulation, and Policy 9.3 Adopting Generative AI in NextG Communications: Case Studies 9.3.1 Integration of Generative AI and Physical Communications Models 9.3.2 Trustworthy Generative AI for Distributed Wireless Communications 9.4 Summary Bibliography 10 Future Research Directions 10.1 Introduction 10.2 Emerging Foundational Research Frontiers 10.2.1 Dedicated GenAI models for communication systems 10.2.2 Fusion of GenAI and Emerging Technologies 10.3 Enhancing Generative AI Models for Wireless Communication Systems 10.3.1 Model Optimization and Generalization 10.3.2 Energy Efficiency 10.3.3 Generative AI for Spectrum Management 10.3.4 AI-driven Network Management and Orchestration 10.3.5 Security and Privacy Concerns 10.4 Practical Case Studies 10.4.1 AI-Powered Network Optimization by T-Mobile 10.4.2 DeepSig’s Generative AI for Wireless Communications 10.5 Conclusion Bibliography

About the Author :
Diep N. Nguyen 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. Nam H. Chu 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. Dinh Thai Hoang is a Faculty Member at the University of Technology Sydney (UTS), Australia. Octavia A. Dobre is a Professor and Canada Research Chair Tier-1 at Memorial University, Canada. Dusit Niyato is a President's Chair Professor in Computer Science and Engineering in the College of Computing and Data Science at Nanyang Technological University, Singapore. Petar Popovski 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.