Predictive QoS Bridge Scheduling for Wireless TSN
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Integrating wireless network devices, such as cellular radiofrequency networks, into Time Sensitive Networks (TSN) poses challenges due to varying Quality of Service (QoS) capabilities caused by environmental mobility and changing radio conditions, leading to under-optimized resource utilization and inefficient scheduling.
Innovation Solution
A method involving a radiofrequency cellular network entity predicts current QoS capabilities and transmits these predictions to a TSN entity for scheduling transmission times, considering a prediction horizon to ensure accurate and dynamic scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wireless network devices are integrated into TSN with predetermined scheduling, then end-to-end latency guarantees can be achieved, but resource utilization becomes under-optimized due to varying QoS capabilities
Solution Approach 1:
The patent implements dynamic scheduling by introducing prediction horizons that allow the system to adapt scheduling decisions based on predicted future QoS conditions. The schedule is no longer static but evolves based on predicted radio conditions, terminal mobility, and QoS variations, resolving the contradiction between reliability guarantees and resource utilization optimization.
Solution Approach 2:
The system performs preliminary QoS capability predictions for future time windows before finalizing scheduling decisions. By predicting QoS conditions in advance (prediction horizon) and preparing scheduling decisions beforehand, the system can optimize resource allocation while maintaining latency guarantees, addressing both reliability and productivity requirements.
2Device complexity
If QoS capabilities are assumed guaranteed for ever in state-of-the-art TSN, then configuration loop time can be long, but this assumption is not adapted to wireless networks where radio conditions evolve in space and time
Solution Approach 1:
The patent implements periodic updating of QoS capability predictions at defined prediction horizons. Instead of assuming static QoS capabilities, the system periodically re-evaluates and updates predictions based on evolving radio conditions, terminal positions, and network state, enabling adaptation to wireless environments while maintaining manageable configuration complexity.
Solution Approach 2:
The system incorporates feedback mechanisms where actual QoS measurements and predictions inform future scheduling decisions. The prediction horizon concept allows the system to use feedback from predicted QoS conditions to adjust scheduling parameters dynamically, resolving the contradiction between configuration simplicity and wireless adaptability.
3Reliability
If worst-case QoS capabilities are used for scheduling computation, then latency guarantees are maintained, but resource utilization becomes inefficient
Solution Approach 1:
The patent applies local quality by making scheduling decisions based on locally predicted QoS conditions for specific time windows and spatial locations rather than using global worst-case assumptions. Each scheduling decision is tailored to the local predicted conditions, improving resource utilization while maintaining latency guarantees where needed.
Solution Approach 2:
The system dynamically changes scheduling parameters based on predicted QoS conditions. Instead of using fixed worst-case parameters, the scheduling algorithm adjusts parameters according to predicted radio conditions, terminal mobility patterns, and QoS variations, achieving better resource utilization while maintaining reliability through prediction-based adaptation.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
The present disclosure relates to a method performed by an entity of a radiofrequency cellular network (5GS), for generating and forwarding data to schedule transmission times of intermediary bridges between end stations communicating with each other via a time sensitive network (TSN), at least one bridge of said intermediary bridges being a radiofrequency bridge involving said radiofrequency cellular network (5GS). More particularly, at least upon reception of a request from an entity (CNC) of the time sensitive network (TSN), said radiofrequency cellular network entity determines a capability prediction Qp of a current Quality of Service of said wireless bridge, and transmits at least data of said prediction Qp to said entity (CNC) of the time sensitive network (TSN) in view to schedule said transmission times, based on said capability prediction Qp and further on a prediction horizon Hp corresponding to a validity duration of the QoS capability prediction Qp.