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Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks - A Reinforcement Learning Perspective (Hardcover, 1st ed. 2020)
Loot Price: R2,957
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Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks - A Reinforcement Learning Perspective (Hardcover, 1st ed. 2020)
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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.
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