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Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks

A Reinforcement Learning Perspective

Produktform: E-Buch Text Elektronisches Buch in proprietärem

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. weiterlesen

Elektronisches Format: PDF

Sprache(n): Englisch

ISBN: 978-9811511202 / 978-9811511202 / 9789811511202

Verlag: Springer Singapore

Erscheinungsdatum: 06.11.2019

Seiten: 136

Autor(en): Bin Jiang, Kun Xu, Yuhua Xu, Qihui Wu, Zhiyong Du

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