O-RAN Congestion Mitigation via Core-RAN Correlation
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Solution Overview
Problem
Existing congestion mitigation techniques in O-RAN-based communication networks often fail to predict congestion accurately and may trigger mitigation actions too late, leading to prolonged user experience degradation.
Innovation Solution
A method that evaluates the temporal behavior of congestion indicators for individual cells, correlates session-related information from the core network domain with RAN information, and triggers congestion mitigation actions based on derived quality indicators to proactively address potential congestion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing congestion mitigation techniques are used in O-RAN-based communication networks, then congestion can be addressed, but the prediction accuracy is insufficient and mitigation actions are triggered too late, leading to prolonged user experience degradation
Solution Approach 1:
The patent applies preliminary action by proactively identifying cells prone to congestion before actual congestion occurs. The system evaluates temporal behavior of congestion indicators and correlates session-related information with RAN information to predict potential congestion scenarios in advance, enabling early mitigation actions that prevent user experience degradation rather than reacting after congestion has already impacted users
Solution Approach 2:
The patent introduces another dimension by correlating data from two different network domains: core network domain (session-related information) and RAN domain (RAN information). This multi-dimensional correlation approach enhances prediction accuracy by combining insights from both domains, allowing the system to identify congestion patterns that would be invisible when analyzing only a single domain
2Reliability
If congestion mitigation actions are triggered early based on predicted congestion, then user experience degradation is reduced, but false predictions may lead to unnecessary mitigation actions
Solution Approach 1:
The patent applies segmentation by dividing the congestion prediction process into distinct functional components: evaluating temporal behavior of congestion indicators, identifying candidate cells prone to congestion, correlating session-related information with RAN information, and deriving quality indicators. This segmented approach allows each component to be optimized independently and reduces overall system complexity by breaking down the complex prediction task into manageable segments
Solution Approach 2:
The patent utilizes parameter changes by monitoring temporal behavior of congestion indicators over time and using these changing parameters to predict future congestion states. The system tracks how congestion indicators evolve and uses these dynamic parameter changes to trigger mitigation actions only when predicted quality indicators suggest actual congestion is likely, reducing false predictions
Data Source
AI summary
A technique of triggering one or more congestion mitigation actions in a communication network is presented. The communication network comprises a core network domain and a cellular RAN domain having an O-RAN architecture. A method implementation comprises evaluating a temporal behavior of at least one congestion indicator for individual cells in the RAN domain to identify one or more candidate cells that are prone to suffering from congestion. The method also comprises correlating, for at least one of the one or more candidate cells, session-related information from the core network domain with RAN information from the RAN domain to derive at least one quality indicator for the at least one candidate cell. Further, the method comprises triggering, dependent on the at least one quality indicator derived for the at least candidate cell, one or more congestion mitigation actions.