BLINC uses large language models to guide Bayesian network causal learning for RAN parameter optimization, delivering 63.5% throughput gains and 19.7% block error rate reduction over data-only baselines in a private 5G testbed while enabling interpretable, adaptive models.
2510.13031 , archivePrefix=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ACCoRD trains an ANN with PPO-Clip reinforcement learning to select conflict resolution actions in O-RAN, reducing negative network events versus rule-based methods in medium and high traffic simulations.
citing papers explorer
-
BLINC: Context-Specific Causal Learning for Automated RAN Configuration
BLINC uses large language models to guide Bayesian network causal learning for RAN parameter optimization, delivering 63.5% throughput gains and 19.7% block error rate reduction over data-only baselines in a private 5G testbed while enabling interpretable, adaptive models.
-
ACCoRD: Actor-Critic Conflict Resolution with Deep learning for O-RAN xApps
ACCoRD trains an ANN with PPO-Clip reinforcement learning to select conflict resolution actions in O-RAN, reducing negative network events versus rule-based methods in medium and high traffic simulations.