Wireless Terminal AI/ML Issue Reporting for Heat and Resource Control
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Solution Overview
Problem
Existing wireless communication systems face challenges in managing AI/ML model-related issues such as overheating and resource limitations, leading to performance degradation and inefficiencies due to inadequate coordination between the UE and network.
Innovation Solution
The wireless terminal determines performance conditions influenced by active AI/ML models and generates reports to the network, allowing for coordinated remedial actions, including network-proposed or terminal-proposed adjustments to address these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI/ML models are deployed in wireless terminals to enhance communication performance, then system intelligence and performance are improved, but terminal overheating and resource constraints worsen
Solution Approach 1:
The patent implements a feedback mechanism where the wireless terminal monitors its internal state (temperature, resource usage) and reports to the network. The network then adjusts AI/ML model configurations based on this feedback, creating a closed-loop system that prevents overheating while maintaining performance optimization.
Solution Approach 2:
The system dynamically adjusts AI/ML model operations based on real-time terminal conditions. When overheating is detected, the network can modify model parameters, switch between models, or adjust execution frequency, making the system adaptable to changing thermal and resource conditions.
2Adaptability or versatility
If complex AI/ML models are executed in wireless terminals to improve processing capabilities, then intelligence and functionality are enhanced, but device resource consumption increases
Solution Approach 1:
The network can modify AI/ML model parameters such as model size, complexity, and computational requirements based on terminal resource status. This allows the system to adjust the balance between processing capability and energy consumption by changing operational parameters rather than hardware.
Solution Approach 2:
The patent divides AI/ML processing into multiple components that can be selectively executed. The terminal can run simpler models for basic functions and only invoke more complex models when necessary, segmenting the computational load to match available resources.
3Reliability
If AI/ML models are continuously updated and optimized to maintain performance, then model accuracy and effectiveness are improved, but terminal computational burden and overheating worsen
Solution Approach 1:
The network acts as an intermediary that performs complex model updates and optimizations remotely. Instead of the terminal continuously retraining and updating models (which would increase computational burden), the network handles these tasks and delivers updated models to the terminal, reducing terminal complexity while maintaining performance.
Data Source
AI summary
A wireless terminal communicates over a radio interface with a radio network. The wireless terminal comprises processor circuitry and interface circuitry. The processor circuitry is configured to determine an event which pertains to a wireless terminal performance condition which is affected/influenced at least in part by an active Artificial Intelligence/Machine Learning (AI/ML) model or functionality of the wireless terminal and to generate a report message which pertains to the event. The interface circuitry configured is to transmit the report message over the radio interface to the radio network.