An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments

Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users s...
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Autor*in:	Xiaohong Li [verfasserIn] Ru Cao [verfasserIn] Jianye Hao [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	network selection dynamic bandwidth reinforcement learning prediction method

Übergeordnetes Werk:	In: Entropy - MDPI AG, 2003, 20(2018), 4, p 236
Übergeordnetes Werk:	volume:20 ; year:2018 ; number:4, p 236

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/e20040236

Katalog-ID:	DOAJ086573993

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allfieldsSound	10.3390/e20040236 doi (DE-627)DOAJ086573993 (DE-599)DOAJ11d9a7395beb4e49a88cd75434357a19 DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Xiaohong Li verfasserin aut An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices). Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users. network selection dynamic bandwidth reinforcement learning prediction method Science Q Astrophysics Physics Ru Cao verfasserin aut Jianye Hao verfasserin aut In Entropy MDPI AG, 2003 20(2018), 4, p 236 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:20 year:2018 number:4, p 236 https://doi.org/10.3390/e20040236 kostenfrei https://doaj.org/article/11d9a7395beb4e49a88cd75434357a19 kostenfrei http://www.mdpi.com/1099-4300/20/4/236 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2018 4, p 236
language	English
source	In Entropy 20(2018), 4, p 236 volume:20 year:2018 number:4, p 236
sourceStr	In Entropy 20(2018), 4, p 236 volume:20 year:2018 number:4, p 236
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	network selection dynamic bandwidth reinforcement learning prediction method Science Q Astrophysics Physics
isfreeaccess_bool	true
container_title	Entropy
authorswithroles_txt_mv	Xiaohong Li @@aut@@ Ru Cao @@aut@@ Jianye Hao @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
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callnumber-first	Q - Science
author	Xiaohong Li
spellingShingle	Xiaohong Li misc QB460-466 misc QC1-999 misc network selection misc dynamic bandwidth misc reinforcement learning misc prediction method misc Science misc Q misc Astrophysics misc Physics An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments
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topic_title	QB460-466 QC1-999 An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments network selection dynamic bandwidth reinforcement learning prediction method
topic	misc QB460-466 misc QC1-999 misc network selection misc dynamic bandwidth misc reinforcement learning misc prediction method misc Science misc Q misc Astrophysics misc Physics
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title	An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments
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title_full	An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments
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title_auth	An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments
abstract	Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices). Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users.
abstractGer	Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices). Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users.
abstract_unstemmed	Networks will continue to become increasingly heterogeneous as we move toward 5G. Meanwhile, the intelligent programming of the core network makes the available radio resource be more changeable rather than static. In such a dynamic and heterogeneous network environment, how to help terminal users select optimal networks to access is challenging. Prior implementations of network selection are usually applicable for the environment with static radio resources, while they cannot handle the unpredictable dynamics in 5G network environments. To this end, this paper considers both the fluctuation of radio resources and the variation of user demand. We model the access network selection scenario as a multiagent coordination problem, in which a bunch of rationally terminal users compete to maximize their benefits with incomplete information about the environment (no prior knowledge of network resource and other users’ choices). Then, an adaptive learning based strategy is proposed, which enables users to adaptively adjust their selections in response to the gradually or abruptly changing environment. The system is experimentally shown to converge to Nash equilibrium, which also turns out to be both Pareto optimal and socially optimal. Extensive simulation results show that our approach achieves significantly better performance compared with two learning and non-learning based approaches in terms of load balancing, user payoff and the overall bandwidth utilization efficiency. In addition, the system has a good robustness performance under the condition with non-compliant terminal users.
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title_short	An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments
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