MDAF Analytics Service Variations for 5G Network Automation
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Solution Overview
Problem
Existing communication systems lack efficient mechanisms for managing and utilizing management data analytics services, particularly in 3GPP TR 23.791 V16.2.0 and 3GPP TR 23.700-91, to ensure slice service level agreements, real-time data analytics, and interaction with ML models for network automation in 5G systems.
Innovation Solution
A management data analytics function (MDAF) is introduced, comprising software components that provide statistics, predictions, and recommendations based on management data, utilizing mathematical, AI/ML, or rule-based procedures, and support multiple Analytics Service Instances (ASIs) with varying context options to cater to specific use cases, enabling efficient computation and reporting of analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple Analytics Service Instances (ASIs) with varying context options are supported, then adaptability and versatility are improved, but device complexity increases
Solution Approach 1:
The patent segments the analytics service into multiple independent Analytics Service Instances (ASIs), each capable of handling specific analytics tasks with defined context options. This segmentation allows the system to support diverse analytics requirements through modular instances rather than a monolithic complex system, resolving the contradiction by organizing complexity into manageable, adaptable units.
Solution Approach 2:
The MDAF apparatus is designed with multi-functional capability to support multiple ASIs simultaneously, where a single apparatus can handle various analytics service types (network slice analytics, mobility analytics, energy analytics, etc.). This universality allows one device to perform multiple analytics functions, improving adaptability without proportionally increasing device complexity.
2Productivity
If real-time and near-real-time analytics computation is performed, then productivity is improved, but use of energy increases
Solution Approach 1:
The patent implements periodic action through configurable reporting intervals and event-triggered analytics computation. Instead of continuous real-time processing, the system computes analytics at defined intervals or when specific events occur, reducing energy consumption while maintaining productivity through timely periodic updates rather than constant processing.
Solution Approach 2:
The system dynamically adjusts analytics computation based on network conditions, service level agreement requirements, and event triggers. Analytics processing intensity varies dynamically - higher when service level agreements require strict compliance or events occur, lower during stable periods - optimizing the balance between productivity and energy consumption.
Data Source
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AI summary
A technique, comprising: directing to a component implementing a function of a communications system information about at least one data analytics service provided by at least one management data analytic function of the communications system; wherein the information about at least one data analytics service comprises information about variation options for the data analytics service.