Centralized Operational Experience Database for SON Optimization
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Solution Overview
Problem
Current cognitive network management systems lack the ability to effectively record and utilize operational experiences from Self-Organizing Networks (SON) functions, leading to unawareness of impacts on network performance and customer experience, which hinders predictive decision-making and optimization.
Innovation Solution
A method and apparatus for collecting, processing, and storing operational experience data, including performance and context data, to calculate diagnosis values and predict outcomes of planned operations, thereby enabling informed decision-making by linking similar experiences and applying effective parameters for future operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SON functions execute operations to optimize network performance, then network performance may be improved, but performance degradation may occur with high probability under certain network circumstances
Solution Approach 1:
The system performs preliminary analysis of operational experiences and network circumstances before executing SON operations. By storing and analyzing historical operation outcomes in advance, the system can predict whether a planned operation will cause performance degradation and prevent its execution, thereby avoiding harmful effects before they occur.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting operational experience data from SON functions, analyzing the outcomes of executed operations, and using this information to influence future operation decisions. The feedback loop enables the system to learn from past performance degradations and improvements, adjusting operation execution strategies accordingly.
2Extent of automation
If operational experience data is collected and stored from SON functions, then predictive decision-making capability is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of predictive decision-making into distinct functional modules: data collection from SON functions, data storage in databases, data analysis processes, and operation decision-making. This segmentation allows each module to be developed and maintained independently, reducing overall system complexity while enabling advanced predictive capabilities.
Solution Approach 2:
The system introduces intermediary components such as databases and analysis entities that mediate between SON functions and the decision-making process. These intermediaries buffer and structure the operational experience data, making it manageable and accessible for predictive analysis without requiring direct complex interactions between all system components.
3Ease of operation
If SON functions operate autonomously without sharing operational experience, then ease of operation is maintained, but network optimization effectiveness decreases
Solution Approach 1:
The system creates a universal operational experience database that serves multiple SON functions and network optimization scenarios. By storing operational experiences in a centralized, accessible format, the system enables different SON functions to benefit from shared knowledge without sacrificing their autonomous operation capabilities, thereby improving overall network optimization effectiveness.
Data Source
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AI summary
The present invention provides apparatuses, methods, computer programs, computer program products and computer-readable media regarding building and applying operational experiences for CM operations. The present invention comprises collecting operational experience information on executed operations from various logical entities via an interface, the collected operational experience information including execution data, and performance data concerning the executed operation, analyzing the operational experience information of the executed operations, storing the operational experience information of the executed operations in the central entity, and arranging the operational experience information of executed operations into different groups in the central entity according to similarities in the analyzed execution data of the operational experience information.