Data-centric AI
Integrated Computation and Communications for Extended Reality
Buch in deiner Nähe kaufen
Beschreibung
This book explores how integrated computation and communication can be achieved to support scalable and user-centric Extended Reality (XR) service provisions. Leveraging the digital twin technique as a key enabler, this book aims to introduce a novel data-centric AI framework to support XR from the perspective of communication and networking. Specifically, the authors present architectural designs and algorithmic solutions of data-centric AI that support the cross-layer and intelligent collection, processing, and analysis of XR user data, thereby enabling user-centric service provision. The book presents a digital twin-based framework that encompasses conceptual architecture, workflow, and operation functions to support diverse XR modules involving both communication and computation. In addition, the book explores the role of data-centric AI in XR resource provisioning, with a particular focus on how data-centric AI enhances both the quality and quantity of data available for AI model training and decision-making. Various learning paradigms, including supervised learning and reinforcement learning, are examined to demonstrate how AI enhances the efficiency and adaptability of resource management.
- Presents research on enabling integrated computation and communications for extended reality (XR) applications;
- Covers multiple functional modules such as pose tracking, rendering, environment mapping, computation, and storage;
- Captures the complex interactions between user behavior patterns, network dynamics, and resource demands.
This book explores how integrated computation and communication can be achieved to support scalable and user-centric Extended Reality (XR) service provisions. Leveraging the digital twin technique as a key enabler, this book aims to introduce a novel data-centric AI framework to support XR from the perspective of communication and networking. Specifically, the authors present architectural designs and algorithmic solutions of data-centric AI that support the cross-layer and intelligent collection, processing, and analysis of XR user data, thereby enabling user-centric service provision. The book presents a digital twin-based framework that encompasses conceptual architecture, workflow, and operation functions to support diverse XR modules involving both communication and computation. In addition, the book explores the role of data-centric AI in XR resource provisioning, with a particular focus on how data-centric AI enhances both the quality and quantity of data available for AI model training and decision-making. Various learning paradigms, including supervised learning and reinforcement learning, are examined to demonstrate how AI enhances the efficiency and adaptability of resource management
Presents research on enabling integrated computation and communications for extended reality (XR) applications Covers multiple functional modules such as pose tracking, rendering, environment mapping, computation, and storage Captures the complex interactions between user behavior patterns, network dynamics, and resource demands
Autor*in
Conghao Zhou
Themen in »Data-centric AI«
Extended Reality Augmented Reality Virtual Reality Device Pose Tracking Simultaneous localization and mapping Mobile Edge Computing Computing Offloading Multi-tier Computing Resource Management Data-centric Artificial Intelligence Digital Twin