Predicting Conflict Impact on Performance in O-RAN

2026-03-09

AI Summary

While existing methods can detect conflicts among intelligent agents in O-RAN, they struggle to predict the actual impact of such conflicts on radio access network (RAN) performance, particularly when agents generate control actions at heterogeneous temporal frequencies. This work proposes an end-to-end framework for conflict impact prediction that, for the first time, explicitly incorporates agent action frequency into the evaluation process. By integrating statistical behavioral profiling, quantification of conflict severity, and a weighted temporal modeling scheme that assigns higher influence to high-frequency control agents, the framework captures the nuanced dynamics of multi-agent interactions. Experimental results demonstrate that the proposed approach accurately predicts the degree of RAN performance degradation caused by conflicting multi-objective intelligent agents, substantially improving both the accuracy and practical utility of impact assessment in O-RAN environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Modeling other Agents
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• O-RAN
• conflict impact
• autonomous agents
• RAN performance
• multi-timescale control

Innovation

• O-RAN
• conflict impact prediction
• autonomous agents
• RAN performance
• multi-timescale control