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.
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2 Pith papers cite this work. Polarity classification is still indexing.
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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.
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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.