Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection
文献类型：期刊论文
| 作者 | Emanuele De Santis; Alessandro Giuseppi; Antonio Pietrabissa; Michael Capponi; Francesco Delli Priscoli |
| 刊名 | Machine Intelligence Research
 |
| 出版日期 | 2022 |
| 卷号 | 19期号:2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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55937]  |
| 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,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. |
入库方式： OAI收割
来源：自动化研究所
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