Self-Organizing Network Adaptation via Big Data Analytics
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Solution Overview
Problem
Self-organizing network (SON) systems face limitations in integrating multi-vendor functions and adapting to changing network conditions due to partitioned functionalities, leading to the need for human intervention in managing communication networks.
Innovation Solution
A method that utilizes big data analytics to adapt and tune SON functions by analyzing data from communication networks, combining the capabilities of big data systems with SON systems to improve performance and automate operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If SON functions are partitioned into individual functions with narrow scope, then ease of operation and modular maintenance are improved, but adaptability to changing network conditions and integration of multi-vendor functions deteriorate
Solution Approach 1:
The patent combines multiple individual SON function instances into a coordinated system where they share common knowledge bases, data streams, and policy frameworks. This merging enables the system to maintain modular benefits while achieving collective adaptability through shared learning and coordinated decision-making across vendor boundaries.
Solution Approach 2:
The patent creates a universal SON coordination framework that can handle multiple function types (self-configuration, self-optimization, self-healing) and multi-vendor implementations through common interfaces and shared knowledge repositories. This universal layer enables any SON function instance to benefit from collective intelligence regardless of its specific scope or vendor origin.
2Speed
If SON functions operate in distributed fashion with fast reaction, then responsiveness to detected conditions is improved, but integration with conventional network operation mechanisms and multi-vendor coordination deteriorate
Solution Approach 1:
The patent introduces a SON coordination entity that acts as an intermediary between distributed SON function instances and conventional network operation mechanisms. This mediator translates between fast distributed decisions and slower centralized coordination, enabling rapid local responses while maintaining system-wide consistency through standardized communication protocols.
Solution Approach 2:
The patent implements a nested architecture where individual SON function instances operate autonomously at the micro-level with fast reaction times, while being embedded within a broader coordination framework that handles multi-vendor integration and conventional system interfaces at the macro-level. This nesting allows fast local operations without requiring complex point-to-point integration across all system layers.
3Measurement precision
If big data systems are used for detailed network performance analysis, then measurement precision is improved, but processing time and offline operation deteriorate
Solution Approach 1:
The patent pre-processes and structures network data into standardized formats and knowledge bases that can be rapidly queried by SON functions. By organizing data in advance with predefined relationships and metadata, the system enables fast retrieval and analysis without requiring complex real-time processing of raw data, thus achieving both precision and speed.
Data Source
AI summary
A method of adapting operation of self-organizing network functions in a communication network comprising a big data level system and a self-organizing network system and at least one network element is provided, wherein the method comprises adapting the operation of at least one self-organizing network function by using knowledge achieved by analysis performed on the big data level.