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Towards user-centric intelligent net...

Du, Zhiyong.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards user-centric intelligent network selection in 5G heterogeneous wireless networksby Zhiyong Du ... [et al.].
其他題名:	a reinforcement learning perspective /
其他作者:	Du, Zhiyong.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	xii, 136 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	5G mobile communication systems.
電子資源:	https://doi.org/10.1007/978-981-15-1120-2
ISBN:	9789811511202$q(electronic bk.)

Towards user-centric intelligent network selection in 5G heterogeneous wireless networksa reinforcement learning perspective /
Towards user-centric intelligent network selection in 5G heterogeneous wireless networksa reinforcement learning perspective /[electronic resource] :by Zhiyong Du ... [et al.]. - Singapore :Springer Singapore :2020. - xii, 136 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811511202$q(electronic bk.)

Standard No.: 10.1007/978-981-15-1120-2doiSubjects--Topical Terms:

854783
5G mobile communication systems.

LC Class. No.: TK5103.25

Dewey Class. No.: 621.382

Towards user-centric intelligent network selection in 5G heterogeneous wireless networksa reinforcement learning perspective /
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505 0 $a 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.
520 $a 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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