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Journal Article
Deep reinforcement learning for base station switching scheme with federated LSTM‐based traffic predictions
- Authors
- Hyebin Park, Seung Hyun Yoon
- Issue Date
- 2024-06
- Citation
- ETRI Journal, v.46, no.3, pp.379-391
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원
- Language
- English
- Type
- Journal Article
- Abstract
- To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality‐of‐service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short‐term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL‐based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal‐to‐interference noise ratio, handover metrics, and prediction performance.
- KSP Keywords
- BS switching, Base station switching, Deep reinforcement learning, Energy efficiency, Existing Approaches, Mobile networks, Network Architecture, Operation scheme, Reinforcement learning(RL), Small base stations, Switching operation
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
