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Mean Field Game and its Applications in Wireless Networks by Zhu Han (English) H

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ISBN-13
9783030869045
Book Title
Mean Field Game and its Applications in Wireless Networks
ISBN
9783030869045
Subject Area
Computers, Technology & Engineering
Publication Name
Mean Field Game and Its Applications in Wireless Networks
Publisher
Springer International Publishing A&G
Item Length
9.3 in
Subject
Mobile & Wireless Communications, Networking / General, Telecommunications
Publication Year
2021
Series
Wireless Networks Ser.
Type
Textbook
Format
Hardcover
Language
English
Author
Reginald A. Banez, Chungang Yang, Zhu Han, Lixin Li
Item Weight
16 Oz
Item Width
6.1 in
Number of Pages
X, 178 Pages

關於產品

Product Information

This book covers the basic theory of mean field game (MFG) and its applications in wireless networks. It starts with an overview of the current and future state-of-the-art in 5G and 6G wireless networks. Then, a tutorial is presented for MFG, mean-field-type game (MFTG), and prerequisite fields of study such as optimal control theory and differential games. This book also includes a literature survey of MFG-based research in wireless network technologies such as ultra-dense networks (UDNs), device-to-device (D2D) communications, internet-of-things (IoT), unmanned aerial vehicles (UAVs), and mobile edge networks (MENs). Several applications of MFG and MFTG in UDNs, social networks, and multi-access edge computing networks (MECNs) are introduced as well. Applications of MFG covered in this book are divided in three parts. The first part covers three single-population MFG research works or case studies in UDNs including ultra-dense D2D networks, ultra-dense UAV networks, and dense-user MECNs. The second part centers on a multiple-population MFG (MPMFG) modeling of belief and opinion evolution in social networks. It focuses on a recently developed MPMFG framework and its application in analyzing the behavior of users in a multiple-population social network. Finally, the last part concentrates on an MFTG approach to computation offloading in MECN. The computation offloading algorithms are designed for energy- and time-efficient offloading of computation-intensive tasks in an MECN. This book targets advanced-level students, professors, researchers, scientists, and engineers in the fields of communications and networks. Industry managers and government employees working in these same fields will also find this book useful.

Product Identifiers

Publisher
Springer International Publishing A&G
ISBN-10
3030869040
ISBN-13
9783030869045
eBay Product ID (ePID)
28050429723

Product Key Features

Number of Pages
X, 178 Pages
Language
English
Publication Name
Mean Field Game and Its Applications in Wireless Networks
Publication Year
2021
Subject
Mobile & Wireless Communications, Networking / General, Telecommunications
Type
Textbook
Subject Area
Computers, Technology & Engineering
Author
Reginald A. Banez, Chungang Yang, Zhu Han, Lixin Li
Series
Wireless Networks Ser.
Format
Hardcover

Dimensions

Item Weight
16 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Reviews
"In this handbook, the authors provide a guide for researchers who want to use the tools of mean field games to study the future development of wireless networks. ... the authors do provide a basic overview of those topics that might suffice for an ambitious newcomer. ... It also offers a gateway for game theorists looking for the necessary institutional background to apply their tools to relevant research questions in wireless networks." (Thomas Wiseman, zbMATH 1493.91001, 2022)
Number of Volumes
1 Vol.
Illustrated
Yes
Lc Classification Number
Tk5105.5-5105.9
Table of Content
1. Overview of Mean Field Games in Wireless Networks.- 2. Introduction to Mean Field Games and Mean-Field-Type Games.- 3. A Survey of Mean Field Game Applications in Wireless Networks.- 4. Mean Field Game Applications in Ultra-Dense 5G, 6G, and Beyond Wireless Networks.- 5. Multiple-Population Mean Field Game for Social Networks.- 6. Mean-Field-Type Game for Multi-Access Edge Computing Networks.
Copyright Date
2021

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