Terminal Device Inference Failure Reporting for Communication Accuracy
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Solution Overview
Problem
Inference failures in AI/ML models used in communication devices can lead to inaccurate data processing, affecting communication quality, as existing technologies lack effective mechanisms to detect and address such failures in real-time.
Innovation Solution
A method where terminal devices receive downlink information to trigger reports on inference failures to network devices, allowing for timely detection and notification of inference failures in data processing models, enabling improved accuracy in information transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI/ML models are used for data processing in communication devices, then communication performance is improved, but inference failures occur leading to inaccurate data processing
Solution Approach 1:
The patent implements a feedback mechanism where the terminal device monitors the data processing model for inference failures and sends failure indication information back to the network device. This feedback loop enables the network device to detect when the AI/ML model produces inaccurate results and take corrective actions, thus maintaining reliability while preserving the performance benefits of AI/ML processing.
Solution Approach 2:
The patent employs preliminary actions by configuring the terminal device with a data processing model before actual data processing occurs. The terminal device is pre-equipped with monitoring capabilities to detect inference failures before they propagate through the system. This proactive approach allows for early detection and response to accuracy issues while maintaining efficient AI/ML-based communication performance.
2Reliability
If real-time detection of inference failures is implemented, then communication accuracy is improved, but system complexity increases
Solution Approach 1:
The terminal device performs self-monitoring of the data processing model for inference failures using built-in monitoring capabilities. This self-service approach eliminates the need for external monitoring systems, reducing overall system complexity while maintaining real-time detection of accuracy issues. The terminal device autonomously generates failure indication information and communicates it to the network device without requiring additional complex infrastructure.
Data Source
AI summary
Embodiments of the present disclosure relate to methods, devices, and computer readable medium for communication. According to embodiments of the present disclosure, a terminal device receives downlink information for triggering a report from a network device. The terminal device determines whether an inference failure occurs in a data processing model. The terminal device transmits the report to the network device. The report indicates whether the inference failure occurs in the data processing model and/or whether information in the report is generated based on the data processing model. In this way, the network device can be informed about the inference failure, thereby improving accuracy of information transmitted by the terminal device.