ACCoRD: Actor-Critic Conflict Resolution with Deep learning for O-RAN xApps
Researchers have developed a new deep learning approach called ACCoRD to resolve control conflicts within Open Radio Access Networks (O-RAN). This method utilizes an Actor-Critic reinforcement learning algorithm, specifically PPO-Clip, to train an Artificial Neural Network. The system analyzes network data and conflicting decisions to infer optimal conflict resolution actions, with ongoing adjustments based on feedback. Simulations indicate that ACCoRD significantly outperforms traditional rule-based methods in reducing negative network events during medium and high traffic conditions. AI
IMPACT Introduces a novel deep learning method for network conflict resolution, potentially improving efficiency in O-RAN environments.