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Title
Towards user-centric intelligent network selection in 5G heterogeneous wireless networks : a reinforcement learning perspective / Zhiyong Du, Bin Jiang, Qihui Wu, Yuhua Xu, Kun Xu.
ISBN
9789811511202 (electronic book)
9811511209 (electronic book)
9789811511196
9811511195
Publication Details
Singapore : Springer, 2020.
Language
English
Description
1 online resource
Call Number
TK5103.25
Dewey Decimal Classification
621.382
Summary
This book presents reinforcement learning (RL) based solutions for user-centric online network selection optimization. The main content can be divided into three parts. The first part (chapter 2 and 3) focuses on how to learning the best network when QoE is revealed beyond QoS under the framework of multi-armed bandit (MAB). The second part (chapter 4 and 5) focuses on how to meet dynamic user demand in complex and uncertain heterogeneous wireless networks under the framework of markov decision process (MDP). The third part (chapter 6 and 7) focuses on how to meet heterogeneous user demand for multiple users inlarge-scale networks under the framework of game theory. Efficient RL algorithms with practical constraints and considerations are proposed to optimize QoE for realizing intelligent online network selection for future mobile networks. This book is intended as a reference resource for researchers and designers in resource management of 5G networks and beyond.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Available in Other Form
Print version: 9789811511196
Introduction
Learning the Optimal Network with Handoff Constraint: MAB RL Based Network Selection
Learning the Optimal Network with Context Awareness: Transfer RL Based Network Selection
Meeting Dynamic User Demand with Transmission Cost Awareness: CT-MAB RL Based Network Selection
Meeting Dynamic User Demand with Handoff Cost Awareness: MDP RL Based Network Handoff
Matching Heterogeneous User Demands: Localized Cooperation Game and MARL based Network Selection
Exploiting User Demand Diversity: QoE game and MARL Based Network Selection
Future Work.
