NWDAF Analytics for Dynamic RFSP Selection in 5G Networks
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Solution Overview
Problem
Existing mobile communication networks face challenges in efficiently managing radio resources and minimizing interference due to the increasing diversity of terminal types and service requirements, necessitating optimized configuration and automation of network apparatuses.
Innovation Solution
The implementation of a network data analytics function (NWDAF) that collects and processes network analytics information to determine a radio/frequency selection priority (RFSP) index value, aiding policy control functions in automating the selection of optimal radio access technology and frequency bands based on usage patterns and service requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual configuration of network apparatuses is used, then ease of operation is maintained, but productivity decreases due to inability to efficiently manage diverse terminal types and service requirements
Solution Approach 1:
The system enables self-service configuration where the NWDAF automatically collects network data, analyzes usage patterns, and determines optimal RFSP index values without manual intervention. The policy control function automatically applies configurations based on analytics results, allowing the network to manage itself efficiently.
Solution Approach 2:
The system implements a feedback loop where network data is continuously collected, analyzed to generate analytics information, used to determine RFSP index values, and then the results are fed back into network configuration. This closed-loop feedback enables continuous optimization of resource management.
2Reliability
If more network apparatuses are deployed to satisfy high communication requirements, then service quality improves, but operational costs increase
Solution Approach 1:
The system dynamically changes the RFSP index value parameter based on network conditions and usage patterns. By adjusting this key parameter, the network can optimize resource allocation without deploying additional apparatuses, maintaining service quality while reducing operational costs.
Solution Approach 2:
The system transitions from static manual configuration to dynamic automated configuration. The RFSP index values are continuously adjusted based on real-time network analytics, allowing the network to adapt to changing conditions and optimize resource usage dynamically rather than relying on fixed infrastructure.
3Object-affected harmful factors
If radio resources are allocated without analytics information, then device complexity is reduced, but interference increases due to lack of optimized configuration
Solution Approach 1:
The NWDAF acts as an intermediary between network data collection and policy control. It collects raw network data, processes it into meaningful analytics information, and provides this processed information to the policy control function for configuration decisions. This intermediary approach reduces interference by centralizing the analytics processing function.
Data Source
AI summary
The present disclosure relates to: a communication technique merging IoT technology with a 5G communication system for supporting a data transmission rate higher than that of a 4G system, and a system therefor. The present disclosure can be applied to intelligent services (for example, smart home, smart building, smart city, smart car or connected car, healthcare, digital education, retail, security, and safety-related services, and the like) on the basis of 5G communication technology and IoT-related technology. An NWDAF according to an embodiment of the disclosure transmits analytics information or prediction information related to wireless access technology and a frequency band and, additionally, provides information for identifying service demand levels of each terminal. Therefore, a network operator or an entity in charge of same can cause terminals to use a specific wireless access technology or frequency band. Thus, the operator can reduce the power consumed by unnecessary base stations or resources, or can maximize the quality of service experienced by a user through a predicted result.