Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 전상운 | - |
dc.date.accessioned | 2023-05-17T05:10:04Z | - |
dc.date.available | 2023-05-17T05:10:04Z | - |
dc.date.issued | 2023-04 | - |
dc.identifier.citation | IEEE ACCESS, v. 11, Page. 24737.0-24751.0 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/10064273/ | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/180682 | - |
dc.description.abstract | In this paper, we consider the problem of decision making in the context of a dense heterogeneous network with a macro base station and multiple small base stations. We propose a deep Q-learning based algorithm that efficiently minimizes the overall energy consumption by taking into account both the energy consumption from transmission and overheads, and various network information such as channel conditions and causal association information. The proposed algorithm is designed based on the centralized training with decentralized execution (CTDE) framework in which a centralized training agent manages the replay buffer for training its deep Q-network by gathering state, action, and reward information reported from the distributed agents that execute the actions. We perform several numerical evaluations and demonstrate that the proposed algorithm provides significant energy savings over other contemporary mechanisms depending on overhead costs, especially when additional energy consumption is required for handover procedure. | - |
dc.description.sponsorship | The work of Yujae Song was supported by the project titled "Development of polar region communication technology and equipment for Internet of Extreme Things (IoET)" funded by the Ministry of Science and ICT (MSIT). The work of Sung Hoon Lim was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology under Grant NRF-2020R1F1A1074926. The work of Sang-Woon Jeon was supported by NRF funded by the Ministry of Education, Science and Technology, MSIT, under Grant NRF-2020R1C1C1013806. | - |
dc.language | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | Handover | - |
dc.subject | Q-learning | - |
dc.subject | Training | - |
dc.subject | Energy consumption | - |
dc.subject | Decision making | - |
dc.subject | Resource management | - |
dc.subject | Rayleigh channels | - |
dc.subject | Deep learning | - |
dc.subject | centralized training decentralized execution | - |
dc.subject | energy minimization | - |
dc.subject | heterogeneous networks | - |
dc.subject | load balancing | - |
dc.subject | reinforcement learning | - |
dc.title | Handover Decision Making for Dense HetNets: A Reinforcement Learning Approach | - |
dc.type | Article | - |
dc.relation.volume | 11 | - |
dc.identifier.doi | 10.1109/ACCESS.2023.3254557 | - |
dc.relation.page | 24737.0-24751.0 | - |
dc.relation.journal | IEEE ACCESS | - |
dc.contributor.googleauthor | Song, Yujae | - |
dc.contributor.googleauthor | Lim, Sung Hoon | - |
dc.contributor.googleauthor | Jeon, Sang-Woon | - |
dc.sector.campus | E | - |
dc.sector.daehak | 공학대학 | - |
dc.sector.department | 국방정보공학과 | - |
dc.identifier.pid | sangwoonjeon | - |
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