Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection

doi:10.1007/s11633-022-1326-3

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	Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection
	Emanuele De Santis; Alessandro Giuseppi; Antonio Pietrabissa; Michael Capponi; Francesco Delli Priscoli
发表期刊	Machine Intelligence Research
ISSN	2731-538X
	2022
卷号	19 期号:2 页码:127-137
摘要	This paper proposes a deep-Q-network (DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process (MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT (radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load balancing. In particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.
关键词	Network selection HetNet deep reinforcement learning deep-Q-network (DQN) 5G communications
DOI	10.1007/s11633-022-1326-3
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55937
专题	学术期刊_Machine Intelligence Research
作者单位	Department of Computer, Control and Management Engineering “Antonio Ruberti”, University of Rome La Sapienza, Rome 00185, Italy
推荐引用方式 GB/T 7714	Emanuele De Santis,Alessandro Giuseppi,Antonio Pietrabissa,et al. Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection[J]. Machine Intelligence Research,2022,19(2):127-137.
APA	Emanuele De Santis,Alessandro Giuseppi,Antonio Pietrabissa,Michael Capponi,&Francesco Delli Priscoli.(2022).Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection.Machine Intelligence Research,19(2),127-137.
MLA	Emanuele De Santis,et al."Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection".Machine Intelligence Research 19.2(2022):127-137.

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