Towards user-centric intelligent network selection in 5G heterogeneous wireless networks : a reinforcement learning perspective /

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Bibliographic Details
Imprint:Singapore : Springer, 2020.
Description:1 online resource
Language:English
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12602462
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Other authors / contributors:Du, Zhiyong.
ISBN:9789811511202
9811511209
9789811511196
9811511195
Notes:Includes bibliographical references and index.
Print version record.
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.
Other form:Print version: Towards user-centric intelligent network selection in 5G heterogeneous wireless networks. Singapore : Springer, 2020 9811511195 9789811511196
Standard no.:10.1007/978-981-15-1