This book investigates edge intelligence-empowered integrated sensing and communications (ISAC) systems from the perspectives of artificial intelligence (AI) model management and resource management. It first introduces the evolution, core principles, and representative use cases of the ISAC paradigm, and then presents a virtualization-centric framework based on network slicing, digital twins, and model-based and data-driven optimization. Under this framework, this book presents task offloading and edge continual learning approaches for intelligent sensing data processing, as well as proactive and real-time resource management methods to support efficient resource utilization and service satisfaction.
Sensing and communications are essential functions in modern autonomous systems and mobile applications. Future wireless networks are expected to integrate these functions by orchestrating network resources, giving rise to the ISAC paradigm. While this integration improves resource utilization efficiency and delivers mutual performance gains, ISAC systems face challenges in sensing signal processing and network resource management due to richer environmental awareness requirements and dynamic service demand.
By examining the interplay between AI model management and resource management, this book provides customized designs and efficient algorithms to support edge intelligence for ISAC. It serves as a reference for researchers, industry professionals, and graduate students in related fields.
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