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Article

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

by 1, 1 and 2,*
1
School of Computer Science and Technology, Tianjin University, Tianjin 300000, China
2
School of Software, Tianjin University, Tianjin 300000, China
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in the 26th International Conference on Artificial Neural Networks—ICANN 2017.
Entropy 2018, 20(4), 236; https://doi.org/10.3390/e20040236
Received: 8 January 2018 / Revised: 7 March 2018 / Accepted: 24 March 2018 / Published: 29 March 2018
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. View Full-Text
Keywords: network selection; dynamic bandwidth; reinforcement learning; prediction method network selection; dynamic bandwidth; reinforcement learning; prediction method
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MDPI and ACS Style

Li, X.; Cao, R.; Hao, J. An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments. Entropy 2018, 20, 236. https://doi.org/10.3390/e20040236

AMA Style

Li X, Cao R, Hao J. An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments. Entropy. 2018; 20(4):236. https://doi.org/10.3390/e20040236

Chicago/Turabian Style

Li, Xiaohong, Ru Cao, and Jianye Hao. 2018. "An Adaptive Learning Based Network Selection Approach for 5G Dynamic Environments" Entropy 20, no. 4: 236. https://doi.org/10.3390/e20040236

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