ML Inference Error Detection in Wireless Communication
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Solution Overview
Problem
In wireless communication networks, the inaccurate inference results generated by machine learning (ML) models lead to negative impacts such as unsuccessful communication and waste of radio resources, highlighting the need for a solution to detect and handle inference errors.
Innovation Solution
The proposed solution involves a method of communication that includes receiving configurations associated with an ML model, detecting pre-defined events indicating inference errors, and transmitting requests for updating the ML model. This method allows for efficient detection and handling of inference errors, improving communication performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ML model is used for air interface interactions, then communication performance is improved, but inference accuracy deteriorates leading to unsuccessful communication and waste of radio resources
Solution Approach 1:
The patent implements a feedback mechanism where the terminal device monitors the accuracy of ML model inference results and compares them with actual measurement results. When inference errors are detected, the terminal device feeds back information to the network device, which then triggers ML model updates. This closed-loop feedback system continuously improves inference accuracy while maintaining communication performance.
Solution Approach 2:
The patent establishes preliminary configurations for ML model monitoring and error detection before actual communication occurs. The network device pre-configures the terminal device with ML model parameters, monitoring thresholds, and update procedures. This preliminary setup enables the system to quickly detect and respond to inference errors without disrupting ongoing communications.
2Reliability
If ML model inference is performed continuously, then communication reliability is improved, but radio resource consumption increases
Solution Approach 1:
The patent implements periodic monitoring of ML model inference accuracy at predetermined intervals rather than continuous monitoring. The terminal device performs inference accuracy checks at configured time intervals and only initiates model updates when errors are detected during these periodic checks. This periodic approach maintains communication reliability while significantly reducing radio resource consumption compared to continuous monitoring.
Solution Approach 2:
The patent dynamically adjusts monitoring parameters such as inference accuracy thresholds and monitoring intervals based on communication conditions. When communication quality is good, the system increases the threshold and extends intervals to reduce resource usage. When degradation is detected, the system lowers thresholds and increases monitoring frequency, optimizing the balance between reliability and resource consumption.
3Reliability
If ML model updates are performed frequently, then inference accuracy is improved, but device complexity and overhead increase
Solution Approach 1:
The patent implements selective ML model updates based on local inference accuracy conditions at each terminal device. Rather than forcing all devices to update models simultaneously or frequently, each device independently monitors its own inference accuracy and only updates when locally detected errors exceed thresholds. This localized approach improves inference accuracy where needed while minimizing unnecessary update overhead across the network.
Data Source
AI summary
Example embodiments of the present disclosure relate to an effective mechanism for handing the scenario of discontinuous coverage. In this solution, the device in the network may obtain at least one configuration associated with a machine learning (ML) model. In particular, the at least one configuration indicates at least one first duration to obtain both an inference result generated by the ML model and a measurement result corresponding to the ML model. By applying the at least one configuration, the inference error may be detected and the incorrect inference may be well handled thereby.