Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks: A Reinforcement Learning Perspective (1st ed. 2020)

By: and and and and

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Synopsis
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.
Copyright:
2020

Book Details

Book Quality:
Publisher Quality
ISBN-13:
9789811511202
Related ISBNs:
9789811511196
Publisher:
Springer Singapore, Singapore
Date of Addition:
Copyrighted By:
Springer
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet, Technology, Mathematics and Statistics, Communication
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.

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