NWDAF Service Demand Forecasting for Wireless Resource Management
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing resources and forecasting service demand, particularly in dynamic environments like IoT networks, which can lead to suboptimal service quality and resource utilization.
Innovation Solution
The implementation of a network data analytics function (NWDAF) that analyzes demand information, generates expected service usage information, and provides it to mobile edge computing (MEC) and service providers, enabling proactive resource management and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual resource management methods are used in wireless communication systems, then system complexity is reduced, but resource utilization efficiency deteriorates and service demand forecasting capability is insufficient
Solution Approach 1:
The system enables automatic resource management where the network data analytics function autonomously collects service demand information, performs analysis and forecasting, and triggers resource provisioning without manual intervention. This self-service mechanism resolves the contradiction by achieving high resource utilization efficiency through automated systems while maintaining manageable complexity through standardized protocols and modular architecture.
Solution Approach 2:
The network data analytics function performs preliminary analysis of service demand information to forecast future resource requirements before actual service requests occur. This advance forecasting enables proactive resource allocation, improving resource utilization efficiency by preparing resources in advance while keeping system complexity manageable through structured prediction algorithms.
2Productivity
If automated resource provisioning based on service demand analysis is implemented, then resource allocation optimization is improved, but system complexity increases
Solution Approach 1:
The automated resource provisioning system is segmented into distinct functional modules: service demand information collection, data analysis, forecasting, and resource provisioning triggers. Each module operates independently with well-defined interfaces, improving service quality through specialized processing while managing complexity by dividing the system into manageable, reusable components.
Solution Approach 2:
The network data analytics function serves multiple purposes: collecting service demand information, analyzing current service patterns, forecasting future requirements, and triggering resource provisioning. This multi-functional approach improves service quality through comprehensive analysis while reducing management complexity by consolidating functions into a single versatile system rather than multiple separate systems.
3Measurement precision
If comprehensive service demand information collection is performed, then forecasting accuracy is improved, but information processing overhead increases
Solution Approach 1:
The system extracts only the most relevant features and patterns from comprehensive service demand information for forecasting purposes. Rather than processing all raw data, the network data analytics function identifies and extracts key indicators that drive service demand, improving forecasting accuracy while reducing information processing overhead by eliminating redundant data processing.
Solution Approach 2:
The system collects comprehensive service demand information but processes only the essential portions needed for accurate forecasting. This partial processing approach maintains high forecasting accuracy by focusing computational resources on critical analysis while avoiding the excessive overhead of processing every detail of the collected information.
Data Source
AI summary
The present disclosure relates to a 5th generation (5G) or pre-5G communication system for supporting a higher data transmission rate than that of a beyond-4th generation (4G) communication system such as Long-Term Evolution (LTE). According to the present disclosure, a method for operating a network data analytics function (NWDAF) in a wireless communication system comprises the steps of receiving information used for service analysis, receiving service analysis request information related to service use in order to perform the service analysis, generating service analysis information including service prediction information on the basis of the service analysis request information, and transmitting the service analysis information.