NWDAF Dynamic QoE Monitoring and Network Parameter Adjustment
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Solution Overview
Problem
Telecommunications companies face challenges in cost-effectively expanding their networks while improving user experience as demand for mobile bandwidth grows, necessitating a better understanding of customer satisfaction metrics like Quality of Experience (QoE) to meet user expectations.
Innovation Solution
Implementing a network data analytics function (NWDAF) within cellular networks to monitor QoE using key performance indicators (KPIs), adjust network configuration parameters, and optimize quality of service (QoS) attributes in real-time through dynamic slice configuration and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If network capacity is expanded to meet growing mobile bandwidth demand, then user experience and QoE are improved, but network cost and complexity increase
Solution Approach 1:
The patent implements dynamic network slicing that allows the network to adaptively adjust resource allocation and configuration parameters in real-time based on changing traffic conditions and QoE requirements, enabling the network to meet growing bandwidth demands through flexible resource orchestration rather than static capacity expansion
Solution Approach 2:
The system dynamically modifies network configuration parameters such as QoS attributes, resource allocation settings, and slice configurations to optimize network performance and deliver improved user experience without requiring physical network expansion, thereby avoiding increased complexity
2Ease of operation
If real-time QoE monitoring and adjustment is implemented, then user experience is optimized, but system complexity and processing requirements increase
Solution Approach 1:
The patent implements a closed-loop feedback system where QoE metrics are continuously monitored, analyzed, and used to automatically adjust network configuration parameters in real-time, enabling optimized user experience through automated decision-making that reduces the need for complex manual intervention systems
Solution Approach 2:
The network system autonomously performs QoE monitoring, analysis, and parameter adjustment without requiring external manual control, allowing the system to self-optimize user experience while managing its own complexity through automated intelligence rather than human-operated complex systems
3Reliability
If dynamic network configuration adjustment is performed to meet QoE thresholds, then QoE is improved, but network control complexity increases
Solution Approach 1:
The system pre-configures multiple network parameter settings and slice configurations that can be rapidly deployed to meet QoE thresholds when needed, allowing the network to maintain reliability through prepared contingency configurations rather than complex real-time calculations
Data Source
AI summary
A method for improving quality of experience (QoE) of a user of a cellular network is disclosed. The QoE of the user is monitored using key performance indicator (KPI) data stored in a quality of service (QoS) parameter database. Using data analytics, network configuration parameters are determined in response to determining that the monitored QoE is below a predetermined threshold set for the user. The determined network configuration parameters are adjusted until the monitored QoE reaches the predetermined threshold.