NWDAF Analytics Generation Using Consumer-Controlled Baselines
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Solution Overview
Problem
Conventional analytics generation in 5G networks lacks consumer control over input parameters, particularly in relation to data quality, quantity, and time-dependencies, leading to inaccurate and delayed analytics outputs, and inefficient consumption by network functions.
Innovation Solution
Implementing a mechanism where network entities like NWDAF generate analytics outputs based on consumer-defined baseline parameters, allowing control over data quantity, quality, and time-dependencies, through operation modes that enable awareness, control, or negotiation of these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If NWDAF generates analytics outputs using pre-defined parameters without consumer input, then the analytics generation process is simple and automated, but the analytics outputs cannot be tailored to specific consumer needs and may lack accuracy
Solution Approach 1:
The system enables dynamic configuration of baseline parameters (data quantity, quality, time-dependencies) according to consumer needs. The NWDAF can adapt its analytics generation process by receiving consumer-specific parameter configurations, allowing the same system to serve different consumers with customized parameters without requiring separate hard-coded logic for each consumer type.
2Area of stationary object
If multiple NWDAF instances are deployed to cover larger areas of interest, then the coverage area is expanded, but the system complexity and resource consumption increase
Solution Approach 1:
A single NWDAF instance is designed to serve multiple consumers with different area of interest requirements by dynamically adjusting its operation parameters. The NWDAF can configure its data collection and processing scope based on consumer-specific baseline parameters, eliminating the need to deploy separate NWDAF instances for different coverage areas while maintaining the ability to serve diverse consumer needs.
3Measurement precision
If the system waits for sufficient data quantity and quality before generating analytics outputs, then the analytics accuracy is improved, but the time delay increases
Solution Approach 1:
The system allows consumers to dynamically adjust baseline parameters including data quantity requirements, quality thresholds, and time-dependencies. By changing these parameters based on consumer-specific needs and priorities, the system can optimize the balance between analytics accuracy and generation time. Consumers with urgent needs can specify lower data quantity thresholds or different quality criteria to reduce wait times, while consumers requiring high accuracy can maintain stricter parameter requirements.
Data Source
AI summary
A consumer of an analytics output receives control and/or knowledge about a set of parameters that is used for generating the analytics output. A set of baseline parameters is used for generating the analytics output, which (a) is associated with a set of analytics consumers and/or with a set of analytics types, and (b) is related to at least one of a statistical property and/or process and/or an output strategy for providing an analytics output. Network entities and corresponding methods for analytics generation and for consuming analytics may be used for generating an analytics output based on the set of baseline parameters.