A DQN agent on a contextual-bandit model, trained with a smooth QoS-threshold reward, selects Rel-18 cell DTX/DRX cycle and on-duration settings, achieving up to 45% simulated energy savings with under 1% average data-rate loss.
Backhaul-aware small cell DTX based on fuzzy Q-Learning in heterogeneous cellular networks,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.NI 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Deep Reinforcement Learning-based Cell DTX/DRX Configuration for Network Energy Saving
A DQN agent on a contextual-bandit model, trained with a smooth QoS-threshold reward, selects Rel-18 cell DTX/DRX cycle and on-duration settings, achieving up to 45% simulated energy savings with under 1% average data-rate loss.