Context Derivation Function for 5G Network Auto-Configuration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current mobile and wireless telecommunication systems, particularly 5G networks, face challenges in automating context-specific network function configuration due to the complexity of managing diverse contexts and conflicting performance objectives, which requires manual effort and is inefficient in adapting to changing network conditions and service level agreements.
Innovation Solution
The implementation of a Context Derivation Function (CDF) that automates the derivation of context-specific network configuration policies and function configurations, using a weighted objective model to prioritize KPI targets and derive context classes, allowing for context-aware auto-configuration of network parameters and behavior adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual configuration of network functions is performed, then configuration accuracy and control over performance objectives can be maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent introduces a Context Derivation Function (CDF) as an intermediary component that automatically derives context information from network data and matches it with appropriate configuration policies. This mediator handles the complexity of diverse contexts and conflicting objectives, enabling automated configuration without requiring manual management of the underlying complexity.
Solution Approach 2:
The system enables self-service automation where the CDF automatically derives contexts, selects appropriate configuration policies, and applies them without human intervention. The network system serves itself by autonomously adapting configurations based on derived contexts and weighted objective models, reducing both manual effort and the perceived complexity for operators.
2Productivity
If automated configuration is implemented, then labor intensity is reduced, but handling diverse contexts and conflicting performance objectives becomes more complex
Solution Approach 1:
The patent transforms the complex problem of managing diverse contexts and conflicting objectives into a parameter-driven approach. The CDF derives contextual parameters from network data, and the system uses weighted objective models with configurable parameters to prioritize competing performance goals. This parameterization enables automated decision-making while maintaining productivity, as the complexity is managed through structured parameter evaluation rather than manual case-by-case handling.
3Reliability
If context-specific configurations are manually managed, then performance optimization can be achieved, but time consumption and manual effort increase
Solution Approach 1:
The system performs preliminary action by pre-defining configuration policies for various contexts and objectives. The CDF derives current contexts and automatically selects the appropriate pre-prepared configuration policies, eliminating the need for manual real-time configuration decisions. This preliminary preparation enables rapid automated adaptation while maintaining performance optimization, as the complex decision-making has been预先 structured into reusable policies.
Data Source
Figure 1~2
Figure 3
Figure 4~5
AI summary
Systems, methods, apparatuses, and computer program products for automating context-specific network function configuration are provided. One method may include receiving an objective model including rules defining key performance indicator (KPI) targets and relative prioritizations of the KPI targets for a communications system. The method may also include automatically determining, using the received objective model, a context model including at least a description of properties of one or more contexts, and generating or selecting at least one of function configuration parameter values (FCVs) or network configuration parameter values (NCPs) according to the context model.