Proactive O-RAN Configuration Auditing via AI Context Analysis
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Solution Overview
Problem
Modern cellular systems face challenges in detecting and addressing configuration inconsistencies and misconfigurations across network functions, leading to varying qualities of service and potential KPI degradation, especially in multi-vendor environments where reactive diagnostics require downtime and are costly.
Innovation Solution
A system and method for dynamic, in-situ, real-time analysis of network function configurations using a processor and memory to detect misconfigurations and predict potential issues based on known contexts, employing an artificial intelligence model and knowledge base to identify and correct configuration mismatches without requiring downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive diagnostics are used to detect configuration inconsistencies, then diagnostic capability is provided, but network downtime is required and costs increase
Solution Approach 1:
The system performs preliminary configuration validation and consistency checks before deployment or during operational phases, proactively identifying configuration inconsistencies before they cause network failures or require reactive diagnostics that would necessitate downtime
Solution Approach 2:
The system implements continuous feedback mechanisms that monitor configuration states in real-time, automatically detecting and alerting to configuration inconsistencies without requiring network downtime, thereby maintaining both diagnostic capability and network availability
2Adaptability or versatility
If multi-vendor network functions are deployed, then network functionality and adaptability are improved, but configuration consistency and quality of service vary
Solution Approach 1:
The system implements a universal configuration management framework that can handle and validate configurations from multiple vendors through a common interface and standardized validation rules, ensuring configuration consistency across diverse network functions while preserving multi-vendor adaptability
Solution Approach 2:
The system enforces homogeneous configuration standards and validation criteria across all network functions regardless of vendor origin, ensuring that configurations meet consistent quality requirements while allowing different vendors to contribute their specialized functionalities
3Productivity
If configuration analysis is performed after provisioning, then deployment speed is maintained, but configuration mismatches and misconfigurations occur
Solution Approach 1:
The system performs configuration analysis and validation as a preliminary step during the provisioning process itself, rather than as a separate post-deployment activity, ensuring configuration accuracy is verified before deployment while maintaining efficient provisioning speeds through integrated validation
Solution Approach 2:
The system implements continuous configuration monitoring and validation throughout the provisioning and operational lifecycle, maintaining both deployment efficiency and configuration accuracy by seamlessly integrating validation into the ongoing operational workflow rather than treating it as a discrete post-deployment step
Data Source
AI summary
Technology described herein can employ dynamically changing network variables and/or context, in realtime, to determine one or more possible contexts that can lead to network function (NF) misconfiguration, NF mismatch and/or key performance indicator (KPI) degradation. In an embodiment, an example system can comprise a processor, and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising determining a working configuration of a network function, and detecting a misconfiguration between a specified configuration and the working configuration of the network function, wherein the detecting is based on a context known to be associated with the misconfiguration. Analysis of context can comprise analyzing, based on an artificial intelligence model, the contexts of the network function as compared to known and/or stored contexts.