Junshan Zhang – författare

A guide to the challenges in making virtual reality, reality

The Metaverse, a version of the internet in which online interactions take place in real time within fully realized virtual spaces, has been promised as the next frontier in wireless communication. It has drawn huge investment from Silicon Valley and widespread media attention. However, the technologies required to make the Metaverse a reality are still in their infancy, and significant barriers must be overcome if this massive step is to be taken.

Realizing the Metaverse provides a systematic overview of these challenges and their likely solutions. Focusing on five key areas—infrastructure, access, intelligence, security, and future developments—it offers one of the first comprehensive, formalized treatments of the Metaverse as a nascent reality. It promises to be an integral contribution to the future development of Metaverse technologies.

Realizing the Metaverse readers will also find:

Realizing the Metaverse is ideal for graduate and advanced undergraduate students in wireless technology, network communications, and related fields, as well as for researchers and industry professionals involved with the Metaverse or adjacent technologies.

A guide to the challenges in making virtual reality, reality

The Metaverse, a version of the internet in which online interactions take place in real time within fully realized virtual spaces, has been promised as the next frontier in wireless communication. It has drawn huge investment from Silicon Valley and widespread media attention. However, the technologies required to make the Metaverse a reality are still in their infancy, and significant barriers must be overcome if this massive step is to be taken.

Realizing the Metaverse provides a systematic overview of these challenges and their likely solutions. Focusing on five key areas—infrastructure, access, intelligence, security, and future developments—it offers one of the first comprehensive, formalized treatments of the Metaverse as a nascent reality. It promises to be an integral contribution to the future development of Metaverse technologies.

Realizing the Metaverse readers will also find:

Realizing the Metaverse is ideal for graduate and advanced undergraduate students in wireless technology, network communications, and related fields, as well as for researchers and industry professionals involved with the Metaverse or adjacent technologies.

With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors'' own research progress on edge intelligence. Specifically, the book first reviewsthe background and present motivation for AI running at the network edge. Next, it provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors'' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. Finally, it discusses the applications, marketplace, and future research opportunities of edge intelligence. This emerging interdisciplinary field offers many open problems and yet also tremendous opportunities, and this book only touches the tip of iceberg. Hopefully, this book will elicit escalating attention, stimulate fruitful discussions, and open new directions on edge intelligence.

This book begins with discussing the fundamentals of Simultaneously Transmitting and Reflecting Surfaces (STARS) from the electromagnetic (EM) and communication perspectives. The basic signal and channel models for employing STARS in wireless communications is introduced as well.  Then, the different categories and possible hardware implementation of STARS are highlighted. Next, the authors focus on attention to the STARS-aided wireless communications. The authors provide a comprehensive performance analysis of STARS with the outage probability, diversity gain and power scaling laws. Moreover, the operating protocols and corresponding beamforming design of STARS are discussed under different phase-shift models. 

As a further advance, the application of machine learning tools in STAR-aided wireless communications is introduced for addressing the beamforming design and resource allocation problems. The novel STARS-aided integrated sensing and communications (ISAC) in future wireless networks are also discussed with several case studies. Within this book, readers will find an extensive exploration of the STARS concept. The content encompasses a thorough survey of STARS research, covering principles, implementation, performance evaluation and applications. By presenting a comprehensive review of the STARS family, this book serves as a valuable resource for gaining insight into the complete pipeline of STARS research. Finally, the authors conclude the book by highlighting several future research directions for STARS. 

This book targets graduate, undergraduate, and postgraduate students as well as researchers working in wireless communications. Wireless communication engineers in industry and government will also want to purchase this book.

This book provides a comprehensive introduction to continual and reinforcement learning for edge AI, which investigates how to build an AI agent that can continuously solve new learning tasks and enhance the AI at resource-limited edge devices. The authors introduce readers to practical frameworks and in-depth algorithmic foundations. The book surveys the recent advances in the area, coming from both academic researchers and industry professionals. The authors also present their own research findings on continual and reinforcement learning for edge AI. The book also covers the practical applications of the topic and identifies exciting future research opportunities.

This book conducts a comprehensive and detailed survey of the recent research efforts in edge intelligence. The authors first review the background and present motivation for AI running at the network edge. The book then provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors'' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. This second edition incorporates the latest research in this rapidly developing area. The authors also highlight the current applications and future research opportunities for edge intelligence.