Near-RT RIC Data Structure Correlation for O-RAN Control
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Solution Overview
Problem
The structure of the Near-RT RIC in O-RAN systems is not sufficiently defined, posing challenges in processing large amounts of data within short control cycles.
Innovation Solution
Correlating classes related to the E2 node with classes related to E2 node component configuration and RAN function, as well as correlating xApp registration configuration with various xApp-related classes, within the Near-RT RIC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the Near-RT RIC collects large amounts of data from multiple RAN nodes and radio units, then the data processing capability is improved, but the control cycle time increases and processing speed deteriorates
Solution Approach 1:
The patent segments the Near-RT RIC into multiple functional modules including data collection module, data processing module, and control execution module. Each module handles specific tasks independently, allowing parallel processing of large data volumes from multiple RAN nodes and radio units without increasing overall control cycle time.
Solution Approach 2:
The patent implements preliminary data filtering and preprocessing at the edge devices (RAN nodes and radio units) before data is transmitted to the Near-RT RIC. This preliminary action reduces the volume of data requiring centralized processing, enabling faster processing speeds while maintaining comprehensive data collection.
2Loss of time
If the Near-RT RIC processes data within a short control cycle, then the control responsiveness is improved, but the data processing completeness deteriorates
Solution Approach 1:
Critical data filtering and aggregation are performed in advance at edge devices and intermediate layers, so that when data reaches the Near-RT RIC, only essential processed information remains. This allows complete processing of necessary data within the short control cycle without information loss.
Solution Approach 2:
Different data types receive different processing priorities and levels of detail. Time-critical data (e.g., radio resource allocation) receives immediate full processing, while less time-sensitive data (e.g., performance monitoring) receives aggregated processing. This local quality differentiation ensures control cycle requirements are met while maintaining processing completeness for each data category.
3Reliability
If the Near-RT RIC structure is highly detailed and defined, then the system reliability is improved, but the device complexity increases
Solution Approach 1:
The Near-RT RIC structure is segmented into standardized functional modules with well-defined interfaces. Each module (data collection, processing, control execution) has specific responsibilities and standardized communication protocols. This segmentation provides detailed structural definition for reliability while avoiding monolithic complexity through modular design.
Solution Approach 2:
The patent defines universal interface standards and data formats that allow different functional modules to interact through common protocols. This universality reduces the complexity of inter-module communication while maintaining detailed structural definitions, as the same interface standards apply across all module interactions regardless of specific function.
Data Source
AI summary
A class related to the xApp registration configuration is correlated to at least one of a class related to the xApp policy, a class related to the xApp messaging configuration, a class related to the xApp enrichment information, and a class related to the xApp API enablement configuration, in the Near-RT RIC (Near-Real Time RAN Intelligent Controller) of the O-RAN. A class related to the xApp registration configuration is correlated to a class related to the xApp RAN function, in the Near-RT RIC (Near-Real Time RAN Intelligent Controller) of the O-RAN.