AI-Enabled TSN Bridge for Indoor Time Synchronization
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Solution Overview
Problem
Traditional TSN networks struggle with maintaining time synchronization accuracy in indoor industrial networks due to factors like signal interference, noise, physical obstructions, and device mobility, leading to decreased communication quality and network inefficiencies.
Innovation Solution
A computer-implemented method and TSN bridge utilizing an AI module that processes SINR, delay, and obstacle data to adjust packet sizes and time information, leveraging machine learning algorithms to enhance synchronization accuracy and network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional TSN networks are used in indoor industrial environments, then basic time synchronization can be achieved, but time synchronization accuracy deteriorates due to signal interference, noise, physical obstructions, and device mobility
Solution Approach 1:
The system performs preliminary calibration by collecting time synchronization data under various known conditions (different SINR levels, obstacle positions, device mobilities) before actual operation. This pre-collected data is used to train machine learning models that can predict and compensate for synchronization errors in real-time, addressing the harmful effects before they degrade performance
Solution Approach 2:
The system continuously monitors actual time synchronization performance and feeds this information back to the machine learning model. The model uses this feedback to adjust its predictions and compensate for ongoing interference and noise effects, maintaining accuracy despite dynamic environmental conditions
2Adaptability or versatility
If traditional TSN networks are used with mobile devices, then device mobility is supported, but time synchronization accuracy deteriorates due to changing network conditions
Solution Approach 1:
The system transitions from static TSN configuration to dynamic adaptation by using machine learning models that continuously adjust synchronization parameters based on real-time device mobility patterns. The model learns how different devices move through the environment and predicts synchronization requirements dynamically, maintaining accuracy despite mobility
Solution Approach 2:
The system changes synchronization parameters (such as timing advance values, packet transmission intervals, and reference signal frequencies) based on predictions from the machine learning model. These parameter adjustments are made in response to detected mobility patterns and environmental changes, maintaining synchronization accuracy across varying conditions
3Measurement precision
If AI module with machine learning algorithms is added to TSN bridge, then time synchronization accuracy is improved, but device complexity increases
Solution Approach 1:
The TSN bridge with AI module performs self-calibration and self-optimization by automatically collecting data, training models, and adjusting parameters without external intervention. The system serves itself by continuously improving its own synchronization performance through embedded machine learning, reducing the need for complex external configuration and management infrastructure
Data Source
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AI summary
Disclosed is a computer-implemented method (600) and a TSN bridge (202) for optimizing performance of an indoor industrial network (100), an indoor industrial network (100) therefor, and a method (600) for predicting network problems in an indoor industrial network (100). The method (600) optimizes the performance of an indoor industrial network (100) by utilizing an AI module (212) in a TSN bridge (202). The AI module (212) receives SINR and Delay information from a sync agent in a mobile router, environmental parameters from sensors, and obstacle data from cameras (136). The AI module (212) identifies potential time accuracy deterioration in device clocks based on the received data. This information is then communicated to a CNC (502) and a CUC (504) for adjustment of data packet size. Finally, a User Plane Function (UPF) sync agent (120) is instructed to send updated time information to the devices for clock adjustments.