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Abstract

The O-RAN Alliance promotes the integration of intelligent autonomous agents to control the Radio Access Network (RAN). This improves flexibility, performance, and observability in the RAN, but introduces new challenges, such as the detection and management of conflicts among the intelligent autonomous agents. A solution consists of profiling the agents before deployment to gather statistical information about their decision-making behavior, then using the information to estimate the level of conflict among agents with different goals. This approach enables determining the occurrence of conflicts among agents, but does not provide information about the impact on RAN performance, including potential service degradation. The problem becomes more complex when agents generate control actions at different timescales, which makes conflict severity hard to predict. In this paper, we present a novel approach that fills this gap. Our solution leverages the same data used to determine conflict severity but extends its use to predict the impact of such conflicts on RAN performance based on the frequency at which each agent generates actions, giving more weight to faster applications, which exert control more frequently. Via a prototype, we demonstrate that our solution is viable and accurately predicts conflict impact on RAN performance.

Citation

Pietro Brach del Prever, Niloofar Mohamadi, Salvatore D’Oro, Leonardo Bonati, Michele Polese, Łukasz Kułacz, Piotr Jaworski, Adrian Kliks, Heiko Lehmann, Tommaso Melodia. 2026. “Predicting Conflict Impact on Performance in O-RAN.” In Proc. IEEE Int. Workshop on 6G AI-RAN: AI Native Distributed Intelligence for 6G Networks. https://arxiv.org/abs/2603.08685.

@inproceedings{brachdelprever2026predicting,
  author    = {Brach del Prever, Pietro and Mohamadi, Niloofar and D'Oro, Salvatore and Bonati, Leonardo and Polese, Michele and Ku{\l}acz, {\L}ukasz and Jaworski, Piotr and Kliks, Adrian and Lehmann, Heiko and Melodia, Tommaso},
  title     = {{Predicting Conflict Impact on Performance in O-RAN}},
  booktitle = {Proc. IEEE Int. Workshop on 6G AI-RAN: AI Native Distributed Intelligence for 6G Networks},
  year      = {2026},
  month     = {3},
  note      = {In print},
  url       = {https://arxiv.org/pdf/2603.08685}
}