Predictive QoS Profile Adaptation Using AI Analytics
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Solution Overview
Problem
Current wireless communication systems face challenges in dynamically adapting Quality of Service (QoS) profiles to ensure service continuity and optimal performance, particularly in scenarios like V2X and eHealth, where predictive maintenance and real-time data transfer are critical, due to the complexity of managing multiple QoS flows and alternatives.
Innovation Solution
The implementation of AI/ML models to configure predictive QoS profiles for QoS flows by analyzing traffic and mobility data, enabling proactive QoS adaptation through AI-assisted QoS-flow-to-QoS-adaptation-pattern mapping, which includes predictive and prescriptive analytics for upgrading or downgrading QoS profiles based on expected conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional QoS management methods are used to manage multiple QoS flows, then service continuity and performance can be maintained, but the system complexity and difficulty of dynamic adaptation increase significantly
Solution Approach 1:
The system performs preliminary actions by predicting future QoS requirements and network conditions in advance using machine learning models. The QoS adaptation pattern is determined proactively based on predicted traffic characteristics and network state, allowing the system to prepare QoS profile changes before they are actually needed, thus maintaining service continuity while simplifying real-time management decisions.
Solution Approach 2:
The patent introduces an intermediary QoS adaptation pattern mechanism that acts as a mediator between raw QoS parameters and actual QoS profile configurations. This adaptation pattern serves as an intermediate layer that translates complex QoS requirements into manageable profile adjustments, reducing the complexity of directly managing multiple QoS flows while ensuring reliable service delivery.
2Productivity
If QoS profiles are dynamically adapted in real-time based on current conditions, then optimal performance can be achieved, but the response time and ability to predict future conditions are limited
Solution Approach 1:
The system determines the QoS adaptation pattern in advance based on predicted future conditions rather than reacting to current changes. Machine learning models analyze historical and current data to forecast traffic characteristics and network conditions, allowing the system to proactively adjust QoS profiles before conditions actually change, thereby improving adaptation efficiency while eliminating reactive delays.
Solution Approach 2:
The QoS adaptation pattern is designed to be dynamic and time-dependent, allowing the system to adjust QoS profiles at different time points based on predicted conditions. The adaptation pattern includes time-stamped QoS profile configurations that automatically become active at appropriate moments, enabling the system to respond optimally to changing conditions without real-time intervention delays.
3Adaptability or versatility
If multiple QoS flows with different priorities are managed simultaneously, then service quality can be differentiated, but the difficulty of configuring and maintaining QoS alternatives increases
Solution Approach 1:
The patent segments QoS management into distinct components: QoS flows, QoS profiles, and QoS adaptation patterns. Each QoS flow can be associated with multiple QoS profiles representing different service levels, and the adaptation pattern selectively activates appropriate profiles based on predicted conditions. This segmentation allows differentiated service quality for multiple flows while simplifying configuration through modular profile management.
Solution Approach 2:
The QoS adaptation pattern mechanism serves multiple functions simultaneously: it manages multiple QoS flows with different priorities, selects appropriate QoS profiles based on predicted conditions, and automates the configuration process. This multi-functional approach enables comprehensive QoS differentiation across diverse service types while maintaining ease of operation through a unified adaptation framework.
Data Source
AI summary
Apparatuses, methods, and systems are disclosed for configuring a predictive QoS adaptation pattern. One apparatus (600) includes an interface (640) that receives (705) a QoS parameter for at least one QoS flow, the at least QoS flow corresponding to at least one UE. The apparatus (600) includes a processor (605) that obtains (710) a data analytics model and (determines 715) an expected QoS profile adaptation pattern comprising at least one QoS profile to be associated with the at least one QoS flow during a first time interval. Here, the data analytics model described at least one expected condition for the at least one UE and/or at least one serving RAN node. Via the interface (640), the processor (605) transmits (720) the expected QoS profile adaptation pattern to at least one network node associated with the QoS flow.