UE-Directed Network Data Collection for AI Optimization
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Solution Overview
Problem
Current network optimization relies heavily on manual debugging, which is laborious, and lacks the global data necessary for effective network performance enhancement using artificial intelligence algorithms.
Innovation Solution
A network data collection method involving a network device sending indication information to user equipment (UE) to record network data, with the UE receiving and recording this data based on the indication, utilizing various message types for instruction and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual debugging is used for network optimization, then network performance can be optimized, but the process is laborious and time-consuming
Solution Approach 1:
The patent replaces manual debugging (mechanical human operation) with automated AI algorithms that analyze network data to identify optimization opportunities, generate optimization actions, and execute them automatically. This substitution eliminates the time-consuming manual process while maintaining or improving network optimization effectiveness.
Solution Approach 2:
The system enables self-service network optimization by automatically collecting network data, analyzing it through AI algorithms, generating optimization actions, and executing them without human intervention. The network system serves itself by autonomously identifying and implementing performance improvements.
2Reliability
If AI algorithms are used for network optimization, then optimization performance can be improved, but large amounts of global data are required which are currently unavailable
Solution Approach 1:
The patent segments the network data collection process by having different network elements (base stations, core network elements) collect and report their local network data to a centralized network data collection system. This segmentation allows global data aggregation from distributed sources, making the required large volumes of network data available for AI analysis without requiring a single centralized data collection point.
3Loss of information
If automated data collection is implemented, then global network data becomes available for AI optimization, but system complexity increases
Solution Approach 1:
The patent implements a universal network data collection system that can collect multiple types of network data (radio resource data, mobility management data, session management data) from various network elements through a standardized interface. This multi-functional system handles diverse data collection requirements through a single unified platform, managing complexity by providing a universal solution rather than separate specialized systems for each data type.
Data Source
AI summary
A network data collection method is provided. The method includes sending first indication information by a network device; in which the first indication information is configured to indicate a user equipment (UE) to record network data. The UE receives the first indication information sent by a network device; and records network data based on the first indication information.