Adaptive QoS/QoE Target Definition in 5G Networks
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Solution Overview
Problem
Existing wireless communication systems struggle to dynamically and adaptively manage Quality of Service (QoS) and Quality of Experience (QoE) parameters for application sessions, especially considering the diverse and dynamic nature of user, network, and traffic contexts.
Innovation Solution
A QoS/QoE target definition function module is introduced, which can be logically part of an enforcement point located in various network locations. This module dynamically defines and enforces QoE targets based on context information, including application, user, network, and traffic conditions, thereby ensuring optimal QoE for application sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static QoS parameters are used for traffic flows, then network management is simplified, but the system cannot adapt to dynamic user, network, and traffic conditions
Solution Approach 1:
The patent implements dynamic QoS parameter adjustment by continuously monitoring network conditions, user context, and traffic characteristics. The system adapts QoS parameters in real-time based on changing conditions, transforming static QoS management into a dynamic process that responds to environmental variations while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The system employs feedback mechanisms where QoS performance is continuously measured and compared against targets. Based on this feedback, the system automatically adjusts QoS parameters to optimize performance. This closed-loop control enables adaptability while keeping management complexity manageable through rule-based or machine learning-driven automated adjustments.
2Reliability
If QoS parameters are dynamically adjusted for each traffic flow, then service quality is optimized, but network management complexity increases significantly
Solution Approach 1:
The patent changes QoS parameters dynamically based on monitored conditions including network load, user context, and traffic characteristics. By adjusting parameters such as bandwidth allocation, priority levels, and latency thresholds in response to changing conditions, the system optimizes service quality while using automated algorithms to manage the complexity of coordinating these changes across multiple traffic flows.
Solution Approach 2:
The system implements self-service mechanisms where QoS parameters are automatically adjusted based on pre-defined policies and real-time monitoring data. The network management system autonomously makes decisions about QoS parameter adjustments without requiring manual intervention, thereby optimizing service quality while containing management complexity through automated rule-based or intelligence-driven decision-making.
3Measurement precision
If context-aware QoS management is implemented, then QoE is enhanced, but processing requirements and system complexity increase
Solution Approach 1:
The patent applies local quality by tailoring QoS parameters to specific traffic flows, users, and network conditions rather than applying uniform parameters globally. Each traffic flow receives customized QoS treatment based on its specific requirements and current context, enhancing QoE measurement accuracy. The system manages complexity by implementing context processing at appropriate network nodes and using selective monitoring focused on relevant parameters for each traffic type.
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AI summary
Methods and apparatus, including computer program products, are provided for QoS/QoE management. In some example embodiments, there may be provided a method. The method may include receiving, at an adaptive quality controller, an indication of a classification of an application, when a session of the application is detected; receiving, in response to the received indication, a policy associated with the classification and context information regarding at least the application; determining, based on the received policy and the context information, a quality of experience target for the session; mapping, by the adaptive quality controller, the quality of experience target to a quality of service parameter; and providing, by the adaptive quality controller, the quality of experience target to an application scheduler and/or the quality of service parameter to a packet scheduler. Related apparatus, systems, methods, and articles are also described.