UE AI Model Differential Reporting for Adaptive Wireless Networks
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Solution Overview
Problem
Existing wireless network systems lack efficient mechanisms for dynamically training and reporting AI models by user equipment (UE) to optimize network functions, leading to obsolete or degraded system performance due to fluctuating environmental attributes.
Innovation Solution
User equipment (UE) is enabled to train and report AI models using configuration parameters and periodicities, with differential reporting and trigger-based actions to maintain model relevance and reduce signaling overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models are frequently updated to maintain relevance with environmental changes, then model accuracy and adaptability improve, but signaling overhead and resource consumption increase
Solution Approach 1:
The patent extracts and reports only the differential changes in AI model parameters rather than transmitting complete models. This selective extraction of changed elements reduces the amount of data that needs to be signaled and transmitted, directly addressing the contradiction between frequent updates and signaling overhead.
Solution Approach 2:
The system implements periodic reporting of AI model differentials at configured intervals or upon triggering conditions. This periodic action allows the system to balance model relevance with resource consumption by updating only when necessary, rather than continuously or too frequently.
2Reliability
If complete AI models are reported frequently, then model relevance is maintained, but network bandwidth and processing resources are consumed
Solution Approach 1:
Instead of reporting complete AI models, the system extracts and reports only the differential portions that have changed. This significantly reduces the data volume requiring network transmission and processing while maintaining model accuracy through selective updates of only the necessary parameter changes.
3Adaptability or versatility
If AI model training is performed continuously, then model adaptability to environmental changes improves, but device energy consumption and computational load increase
Solution Approach 1:
The system performs AI model training and reporting periodically based on configured periodicities or triggering conditions rather than continuously. This periodic operation maintains model adaptability to environmental changes while significantly reducing device energy consumption and computational load by operating only when necessary.
Solution Approach 2:
The UE autonomously determines when to perform model training and reporting based on local conditions and configured parameters. This self-service approach allows the device to optimize its own resource usage by performing computations only when environmental changes warrant model updates, balancing adaptability with energy efficiency.
Data Source
AI summary
The present application relates to devices and components including apparatus, systems, and methods for user equipment-based artificial intelligence model training or reporting.