AI/ML Capability Signaling for Wireless Performance Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless communication systems, particularly in 5G NR, lack mechanisms for real-time evaluation and feedback on the performance of AI/ML models deployed in diverse real-world scenarios, leading to potential performance drifts and inefficiencies.
Innovation Solution
Implement signaling mechanisms for capability feedback and rating, allowing wireless devices to communicate indicators of target and expected performance, measure actual performance, and provide feedback on AI/ML functionalities, enabling continuous validation and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If AI/ML models are deployed in diverse real-world scenarios without real-time evaluation mechanisms, then system complexity is reduced and deployment is simplified, but performance reliability deteriorates due to potential performance drifts
Solution Approach 1:
The patent implements a feedback mechanism where wireless devices exchange capability indicators and performance measurements. The first wireless device provides feedback information to the second wireless device about measured performance, enabling continuous monitoring and adjustment of AI/ML model performance in real-time operation.
Solution Approach 2:
The patent establishes target performance criteria and expected performance indicators before AI/ML models are deployed. These pre-defined benchmarks serve as reference points for subsequent performance evaluation, allowing devices to assess whether model performance meets requirements without requiring complex real-time analysis.
2Reliability
If real-time performance feedback mechanisms are implemented for AI/ML models, then performance reliability is improved through continuous validation, but device complexity increases due to additional signaling requirements
Solution Approach 1:
The patent implements a feedback mechanism where wireless devices exchange capability indicators and performance measurements. The first wireless device provides feedback information to the second wireless device about measured performance, enabling continuous monitoring and adjustment of AI/ML model performance in real-time operation.
Solution Approach 2:
The patent uses standardized performance parameters and indicators that can be measured and exchanged between devices. By defining specific measurable criteria (target performance, expected performance, measured performance), the system simplifies the feedback process to basic parameter comparisons rather than complex analysis.
3Productivity
If AI/ML models are updated to maintain best performance, then productivity is improved through continuous optimization, but operational stability worsens due to potential disruptions
Solution Approach 1:
The patent enables dynamic adjustment of AI/ML model performance parameters based on real-time feedback while maintaining operational continuity. Devices can adapt performance criteria and update models without disrupting ongoing communications, allowing the system to evolve while maintaining stable operation.
Solution Approach 2:
The patent establishes target performance criteria and expected performance indicators before AI/ML models are deployed. These pre-defined benchmarks serve as reference points for subsequent performance evaluation, allowing devices to assess whether model performance meets requirements without requiring complex real-time analysis.
Data Source
AI summary
A method for wireless communication at a first wireless device and related apparatus are provided. In the method, the first wireless device communicates, with a second wireless device, one or more of a first indicator and a second indicator. The first indicator may include a target performance for a performance criterion associated with a functionality of the second wireless device, and the second indicator may include an expected performance for the performance criterion associated with the functionality. The first wireless device further measures the performance based on the functionality; and provides, for the second wireless device, feedback information indicating the measured performance associated with the functionality.