O-RAN xApps Conflict Management using Graph Convolutional Networks
This addresses conflict management for O-RAN network operators, but it is incremental as it applies an existing GCN method to a new domain-specific problem.
The paper tackles the problem of managing conflicts between network applications in O-RAN by introducing GRAPHICA, a GCN-based method that predicts three types of conflicts and identifies root causes, achieving an F1-score over 98% on synthesized datasets with class imbalance.
The lack of a unified mechanism to coordinate and prioritize the actions of different applications can create three types of conflicts (direct, indirect, and implicit). Conflict management in O-RAN refers to the process of identifying and resolving conflicts between network applications. In our paper, we introduce a novel data-driven GCN-based method called GRAPH-based Intelligent xApp Conflict Prediction and Analysis (GRAPHICA) based on Graph Convolutional Network (GCN). It predicts three types of conflicts (direct, indirect, and implicit) and pinpoints the root causes (xApps). GRAPHICA captures the complex and hidden dependencies among the xApps, controlled parameters, and KPIs in O-RAN to predict possible conflicts. Then, it identifies the root causes (xApps) contributing to the predicted conflicts. The proposed method was tested on highly imbalanced synthesized datasets where conflict instances range from 40% to 10%. The model is tested in a setting that simulates real-world scenarios where conflicts are rare to assess its performance. Experimental results demonstrate a high F1-score over 98% for the synthesized datasets with different levels of class imbalance.