Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework

Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the den...
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Autor*in:	Xinwei Wang [verfasserIn] Jiandong Li [verfasserIn] Lingxia Wang [verfasserIn] Chungang Yang [verfasserIn] Zhu Han [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Game theory heterogeneous networks machine learning model-driven network selection

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 7(2019), Seite 21645-21661
Übergeordnetes Werk:	volume:7 ; year:2019 ; pages:21645-21661

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2019.2898205

Katalog-ID:	DOAJ026615460

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source	In IEEE Access 7(2019), Seite 21645-21661 volume:7 year:2019 pages:21645-21661
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authorswithroles_txt_mv	Xinwei Wang @@aut@@ Jiandong Li @@aut@@ Lingxia Wang @@aut@@ Chungang Yang @@aut@@ Zhu Han @@aut@@
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callnumber-first	T - Technology
author	Xinwei Wang
spellingShingle	Xinwei Wang misc TK1-9971 misc Game theory misc heterogeneous networks misc machine learning misc model-driven misc network selection misc Electrical engineering. Electronics. Nuclear engineering Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework
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topic_title	TK1-9971 Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework Game theory heterogeneous networks machine learning model-driven network selection
topic	misc TK1-9971 misc Game theory misc heterogeneous networks misc machine learning misc model-driven misc network selection misc Electrical engineering. Electronics. Nuclear engineering
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title	Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework
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title_sort	intelligent user-centric network selection: a model-driven reinforcement learning framework
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title_auth	Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework
abstract	Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the dense environment of multi-RATs challenges the network selection because of the more frequent and complex decision process along with increased complexity. Introducing artificial intelligence to ultra-dense heterogeneous networks can improve the way we address network selection today, and can execute efficient and intelligent network selection. Whereas, there still exist difficulties to be noted. On one hand, the contradiction between real-time communications and time-consuming learning is exacerbated, which can result in slow convergence. On the other hand, the black-box learning mode suffers from oscillation due to the diversity of multi-RATs, which can result in arbitrary convergence. In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory. Further, we implement a distributed algorithm at the user side based on the proposed framework, which can reduce the number of frequent switching, increase the possibility of gainful switching, and provide the individual service. The simulation results confirm the performance of the algorithm in accelerating convergence rate, boosting user experience, and improving resource utilization.
abstractGer	Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the dense environment of multi-RATs challenges the network selection because of the more frequent and complex decision process along with increased complexity. Introducing artificial intelligence to ultra-dense heterogeneous networks can improve the way we address network selection today, and can execute efficient and intelligent network selection. Whereas, there still exist difficulties to be noted. On one hand, the contradiction between real-time communications and time-consuming learning is exacerbated, which can result in slow convergence. On the other hand, the black-box learning mode suffers from oscillation due to the diversity of multi-RATs, which can result in arbitrary convergence. In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory. Further, we implement a distributed algorithm at the user side based on the proposed framework, which can reduce the number of frequent switching, increase the possibility of gainful switching, and provide the individual service. The simulation results confirm the performance of the algorithm in accelerating convergence rate, boosting user experience, and improving resource utilization.
abstract_unstemmed	Ultra-dense heterogeneous networks, as a novel network architecture in the fifth-generation mobile communication system (5G), promise ubiquitous connectivity and smooth experience, which take advantage of multiple radio access technologies (RATs), such as WiFi, UMTS, LTE, and WiMAX. However, the dense environment of multi-RATs challenges the network selection because of the more frequent and complex decision process along with increased complexity. Introducing artificial intelligence to ultra-dense heterogeneous networks can improve the way we address network selection today, and can execute efficient and intelligent network selection. Whereas, there still exist difficulties to be noted. On one hand, the contradiction between real-time communications and time-consuming learning is exacerbated, which can result in slow convergence. On the other hand, the black-box learning mode suffers from oscillation due to the diversity of multi-RATs, which can result in arbitrary convergence. In this paper, we propose a model-driven framework with a joint off-line and on-line way, which is able to achieve fast and optimal network selection through an alliance of machine learning and game theory. Further, we implement a distributed algorithm at the user side based on the proposed framework, which can reduce the number of frequent switching, increase the possibility of gainful switching, and provide the individual service. The simulation results confirm the performance of the algorithm in accelerating convergence rate, boosting user experience, and improving resource utilization.
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title_short	Intelligent User-Centric Network Selection: A Model-Driven Reinforcement Learning Framework
url	https://doi.org/10.1109/ACCESS.2019.2898205 https://doaj.org/article/c7ce00a905384b57b7c0668ea61c2ac4 https://ieeexplore.ieee.org/document/8653879/ https://doaj.org/toc/2169-3536
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