Predictive Radio Bearer Remapping for QoS Continuity
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Solution Overview
Problem
Existing wireless communication systems struggle to adapt radio bearer configurations predictively to ensure consistent quality of service (QoS) in dynamic network conditions, leading to potential service disruptions and inefficiencies.
Innovation Solution
Implementing predictive radio bearer configuration methods and apparatuses that utilize machine learning (ML) and data analytics to anticipate QoS changes, enabling proactive remapping and configuration of radio bearers based on expected QoS profiles, ensuring optimal mapping patterns for user equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If radio bearer configuration is adapted reactively based on current QoS conditions, then response time to QoS changes is reduced, but service continuity is compromised due to delayed adaptation
Solution Approach 1:
The system performs preliminary actions by predicting future QoS profile patterns using machine learning models and proactively adapting radio bearer configurations before actual QoS changes occur. This predictive approach eliminates the delay inherent in reactive adaptation while ensuring service continuity is maintained through advance preparation of appropriate bearer mappings.
2Reliability
If machine learning models are deployed for predictive QoS analysis, then service continuity is improved through proactive adaptation, but device complexity increases
Solution Approach 1:
The patent introduces intermediary components including a dedicated machine learning module and a QoS pattern analysis module that act as mediators between the radio resource management system and the core network. These intermediaries handle the complex predictive analytics tasks, allowing the core radio bearer management system to remain relatively simple while still achieving proactive adaptation through the intermediary's predictions and recommendations.
3Reliability
If frequent radio bearer remapping is performed to optimize QoS, then quality of service is improved, but network signaling overhead increases
Solution Approach 1:
The system performs preliminary radio bearer remapping based on predicted QoS patterns before actual service degradation occurs. By anticipating QoS changes and proactively configuring appropriate bearer mappings, the system avoids the need for frequent reactive remapping operations, thereby reducing overall signaling overhead while maintaining consistent service quality.
Solution Approach 2:
The machine learning model operates periodically to predict QoS profile patterns and generate remapping recommendations at optimized intervals. This periodic operation allows the system to balance between maintaining up-to-date bearer configurations and minimizing signaling overhead by avoiding excessive or unnecessary remapping operations.
Data Source
AI summary
Apparatuses, methods, and systems are disclosed for predictively adapting a radio bearer configuration. One method includes receiving an expected quality of service profile pattern for at least one quality of service flow for at least one user equipment. The method includes determining a predictive adaption to a radio bearer configuration based on the expected quality of service profile pattern, wherein the predictive adaption comprises at least one radio bearer remapping to the at least one quality of service flow. The method includes configuring, based on the predictive adaption, a predictive quality of service flow to radio bearer mapping pattern for an expected time window. The method includes transmitting the predictive quality of service flow to radio bearer mapping pattern to the at least one user equipment.