Deep reinforcement learning for base station switching scheme with federated LSTM-based traffic predictions

Deep reinforcement learning for base station switching scheme with federated LSTM-based traffic predictions

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 be...

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Bibliographic Details
Main Authors: Hyebin Park, Seung Hyun Yoon
Format: Article
Language:English
Published: Electronics and Telecommunications Research Institute (ETRI) 2024-06-01
Series:ETRI Journal
Subjects:
base station switching
deep reinforcement learning
federated learning
lstm
traffic forecasting
Online Access:https://doi.org/10.4218/etrij.2023-0065

Internet

https://doi.org/10.4218/etrij.2023-0065

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