Near-RT RIC xApp Conflict Mitigation Using Two-Stage ML
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing conflict mitigation approaches in Open Radio Access Network (O-RAN) are inadequate for addressing direct, indirect, and implicit conflicts between xApps on near-RT RIC, leading to network performance degradation, particularly at large scales, and existing solutions like multi-agent team learning increase communication overhead and complexity.
Innovation Solution
Implementing a conflict predictor, detector, and mitigator using machine learning models to identify and resolve conflicts by predicting potential conflicts during registration and detecting actual conflicts during runtime, with priority-based mitigation procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to predict and detect conflicts between xApps, then conflict resolution efficiency is improved, but computational resource intensity increases
Solution Approach 1:
The conflict predictor performs preliminary analysis during xApp registration to identify potential conflicts before they occur in runtime. By predicting conflicts in advance using static information available at registration time, the system avoids the need for continuous complex monitoring during runtime, thereby improving conflict resolution efficiency while minimizing ongoing computational resource consumption.
Solution Approach 2:
The system applies a two-stage approach where only potentially conflicting xApps identified by the predictor undergo full conflict detection analysis. This partial application of the full detection mechanism to only necessary cases reduces overall computational resource intensity while maintaining high conflict resolution efficiency for the critical subset of xApps.
2Measurement precision
If conflict detection is performed continuously at runtime, then conflict detection accuracy is improved, but system complexity increases
Solution Approach 1:
The conflict detection system is segmented into two independent components: a conflict predictor that operates at registration time and a conflict detector that operates at runtime. This segmentation allows each component to be optimized for its specific function, improving overall detection accuracy while managing system complexity through modular design where each segment handles a specific aspect of conflict identification.
Solution Approach 2:
The conflict predictor acts as an intermediary that filters and identifies potentially conflicting xApps before they reach the full conflict detection mechanism. This intermediary layer reduces the number of xApp pairs that require intensive runtime analysis, thereby improving detection accuracy for critical cases while reducing the overall system complexity by avoiding universal continuous monitoring.
3Reliability
If priority-based mitigation procedures are implemented, then network performance is improved, but control mechanism complexity increases
Solution Approach 1:
The system assigns priority parameters to different xApps during registration, which are then used by the conflict mitigator to resolve conflicts. By changing the state space to include priority levels and using these parameters for automated decision-making, the system improves network performance through structured conflict resolution while managing control complexity through parameter-based automation rather than complex rule-based systems.
Data Source
AI summary
Architectures and techniques are described that can provide conflict mitigation techniques for xApp that are executed on a near-realtime radio access network intelligent controller (near-RT RIC). A first, deep learning machine learning model can be employed during registration of the xApps, which can identify potential conflicts and flag those potentially conflicting xApps. A second machine learning model can be employed during run time of the xApps, which can identify whether a control message from a given flagged xApp conflicts with a configuration applied to an E2 node by another flagged xApp based on the current network state. Conflicts can be mitigated based on a priority between the two conflicting xApps.