TSN Bridge Scheduling Using 5G QoS Prediction

Solution Overview

Problem

Integrating wireless network devices, such as 5G cellular networks, into Time Sensitive Networks (TSN) poses challenges due to varying radio conditions and transmission delays, leading to under-optimized resource utilization and inefficient scheduling, as existing methods assume fixed QoS capabilities.

Innovation Solution

The method involves obtaining a Quality of Service (QoS) capability prediction from the 5G network for a specified horizon and scheduling transmission times based on this prediction to adapt to varying wireless conditions, using optimization algorithms and dynamic computation resources to ensure efficient bridge scheduling.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If fixed QoS capabilities are assumed for TSN bridges, then scheduling computation is simplified, but resource utilization becomes under-optimized due to varying radio conditions

Engineering Contradiction:

Improvescheduling computation complexityVSAvoidresource utilization efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The patent applies dynamics by transitioning from static QoS capability assumptions to dynamic QoS capability predictions. The system now obtains predicted QoS capabilities from the 5G network that reflect current radio conditions, allowing the scheduling computation to adapt to varying network states while maintaining computational tractability through predictive modeling rather than continuous re-evaluation.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent applies preliminary action by obtaining QoS capability predictions from the 5G network before performing scheduling computation. These predictions provide advance information about expected network performance, enabling the scheduling algorithm to optimize resource allocation in advance rather than reacting to actual performance degradation after scheduling decisions are made.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If QoS capability predictions are obtained from 5G network, then resource utilization is optimized, but scheduling computation time increases

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidscheduling computation time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by obtaining QoS capability predictions from the 5G network before performing scheduling computation. These predictions provide advance information about expected network performance, enabling the scheduling algorithm to optimize resource allocation in advance rather than reacting to actual performance degradation after scheduling decisions are made.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies mechanics substitution by replacing complex, time-consuming scheduling computations with simplified scheduling decisions based on pre-obtained QoS predictions. Instead of performing extensive optimization calculations, the system uses the predicted capabilities to directly inform scheduling decisions, substituting computational complexity with information-based decision making.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Speed

If traditional scheduling methods are used without QoS predictions, then computation is faster, but transmission delays increase due to inaccurate scheduling decisions

Engineering Contradiction:

Improvescheduling computation speedVSAvoidtransmission delay guarantee

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The patent applies preliminary action by obtaining QoS capability predictions from the 5G network before performing scheduling computation. These predictions provide advance information about expected network performance, enabling the scheduling algorithm to optimize resource allocation in advance rather than reacting to actual performance degradation after scheduling decisions are made.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies feedback by using QoS capability predictions obtained from the 5G network to inform and adjust scheduling decisions. The system continuously monitors predicted QoS capabilities and adapts its scheduling strategy accordingly, creating a feedback loop where network performance information directly influences resource allocation decisions to maintain delay guarantees.

Inventive Principle:

Principle #23Feedback

Data Source

PatentEP4604607A1Time constraint scheduling computation in a time sensitive network

Publication Date: 2025.08.20 MITSUBISHI ELECTRIC R&D CENTRE EUROPE BV

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AI summary

The disclosure relates to a method performed by an entity of a time sensitive network (TSN), to schedule transmission times of intermediary bridges between end stations communicating with each other via the time sensitive network (TSN), at least one bridge of said intermediary bridges being a radiofrequency bridge involving a radiofrequency cellular network (5GS). The entity (CNC) of the time sensitive network (TSN) obtains from the radiofrequency cellular network (5GS) at least a capability prediction Qp of a Quality of Service of said wireless bridge, and schedules 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.