Generative AI for Communications Systems

Diep N. Nguyen , Dinh Thai Hoang , Octavia A. Dobre , Dusit Niyato , Petar Popovski 지음

Wiley-IEEE Press 2026년 01월 05일 출간

작품소개

책소개

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 systemsCybersecurity concerns such as ethics and privacy in relation to Generative AIApplications of Generative AI across various layers, including the PHY layer, MAC layer, Network layer, and Application layerCommunications 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.

목차

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

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인물정보

저자(글) Diep N. Nguyen

저자(글) Dinh Thai Hoang

저자(글) Octavia A. Dobre

저자(글) Dusit Niyato

저자(글) Petar Popovski