Core Network Element Application Function for Data Analysis
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Solution Overview
Problem
Current core network elements have a limited application range as they only provide network data analysis services or monitoring results, restricting their utilization.
Innovation Solution
A wireless communication method and core network elements that enable terminal devices to request and receive analysis results or monitoring data from core network elements, expanding the application range by using data collection application functions and application functions to facilitate data exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a core network element provides only network data analysis services or monitoring results, then the service function is simple and reliable, but the application range is limited
Solution Approach 1:
The core network element is enhanced to perform multiple functions: it not only provides traditional network data analysis services but also collects terminal data, performs federated learning, and provides AI model training services. This multi-functionality expands the application range while maintaining a unified network element structure
Solution Approach 2:
An application function (AF) is introduced as an intermediary between the terminal device and the core network element. The AF facilitates data exchange and coordination, enabling the core network element to serve multiple purposes through a standardized interface while managing the complexity of multiple services
2Quantity of substance
If a core network element collects data from terminal devices directly, then the data collection capability is enhanced, but the device complexity and security risks increase
Solution Approach 1:
The application function (AF) serves as an intermediary that manages data collection from terminal devices. The AF receives data from terminals, processes it according to federated learning requirements, and forwards relevant information to the core network element, thereby reducing the complexity of direct terminal-network element communication
Solution Approach 2:
The data collection and processing function is segmented into multiple stages: terminal data collection, AF data processing and aggregation, and core network element analysis. This segmentation distributes complexity across different network functions while enabling comprehensive data utilization
3Measurement precision
If federated learning is implemented across multiple terminal devices, then the AI model accuracy is improved, but the coordination complexity and communication overhead increase
Solution Approach 1:
Multiple terminal devices are merged into a coordinated federated learning system where each device contributes local data and model updates. The core network element aggregates these contributions to train a global AI model, achieving higher accuracy through combined intelligence while managing coordination through standardized protocols
Solution Approach 2:
A feedback mechanism is established where terminal devices receive model updates from the core network element, apply them locally, and send updated parameters back for aggregation. This iterative feedback loop enables continuous model improvement while maintaining systematic coordination across distributed devices
Data Source
AI summary
Disclosed are a wireless communication method, a terminal device, and a core network element. The method includes: transmitting, by a terminal device, a first request, where the first request is used to request first information; and receiving, by the terminal device, the first information transmitted by a first core network element, where the first information includes an analysis result of a first network data analysis service or a monitoring result of a first event. The first core network element may provide the terminal device with the analysis result of the network data analysis service or the monitoring result of the first event.