Distributed Online Handover Decisions for Energy Efficiency in Dense HetNets
- Authors
- Song,Yujae; Lim,Sung Hoon; Jeon,Sang-Woon
- Issue Date
- Dec-2020
- Publisher
- IEEE
- Citation
- GLOBECOM 2020 - 2020 IEEE Global Communications Conference, v.2020-January, pp 1 - 6
- Pages
- 6
- Indexed
- OTHER
- Journal Title
- GLOBECOM 2020 - 2020 IEEE Global Communications Conference
- Volume
- 2020-January
- Start Page
- 1
- End Page
- 6
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114652
- DOI
- 10.1109/GLOBECOM42002.2020.9348215
- ISSN
- 2334-0983
2576-6813
- Abstract
- In this paper, we consider the problem of handover decision making in the context of a dense heterogeneous network with a macro base station and multiple small base stations. We propose a distributed deep Q-learning based algorithm that minimizes the overall energy consumption by taking into account both the energy consumption from transmission and hand over overheads. The proposed algorithm is performed in a distributed and interactive manner 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 distributed handover agents. We perform several numerical evaluations and demonstrate that the proposed algorithm provides 10% to 30% energy savings over other contemporary handover mechanisms depending on handover overhead costs.
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Collections - COLLEGE OF ENGINEERING SCIENCES > DEPARTMENT OF MILITARY INFORMATION ENGINEERING > 1. Journal Articles
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