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

doi:10.1007/s11633-022-1326-3

CORC > 自动化研究所 > 中国科学院自动化研究所 > 学术期刊 > International Journal of Automation and Computing

	Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection
	Emanuele De Santis, Alessandro Giuseppi, Antonio Pietrabissa, Michael Capponi, Francesco Delli Priscoli
刊名	Machine Intelligence Research
	2022
卷号	19 页码:127-137
关键词	Network selection HetNet deep reinforcement learning deep-Q-network (DQN) 5G communications
ISSN号	2731-538X
DOI	10.1007/s11633-022-1326-3
英文摘要	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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/47400]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
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, Michael Capponi, Francesco Delli Priscoli. Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection[J]. Machine Intelligence Research,2022,19:127-137.
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,19,127-137.
MLA	Emanuele De Santis, Alessandro Giuseppi, Antonio Pietrabissa, Michael Capponi, Francesco Delli Priscoli."Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection".Machine Intelligence Research 19(2022):127-137.