Communication Terminal AI Model Switching to Reduce Signaling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing AI model update methods in communication systems result in high network signaling overhead due to the transmission of updated models.
Innovation Solution
A method and apparatus that enable terminals to activate, switch, or update AI models based on changes in environmental or working status by utilizing model identities associated with first information, such as cell identity, network operator identity, or channel quality, reducing the need for extensive network signaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI model updates are transmitted through the network, then the terminal can obtain updated AI models, but the network signaling overhead increases significantly
Solution Approach 1:
The patent extracts only the essential model identity information from the complete AI model update data. Instead of transmitting full model updates through the network, the system transmits only the model identity that identifies specific model parameters. The terminal then uses this identity to locally retrieve or activate the corresponding AI model, eliminating the need for extensive network signaling while maintaining update capability.
Solution Approach 2:
The patent introduces a model identity as an intermediary between the network and the terminal's AI model storage. This identity acts as a reference or pointer that the terminal uses to access the actual AI model parameters locally. The intermediary enables indirect model updates without direct transmission of model data, significantly reducing signaling overhead.
2Adaptability or versatility
If the terminal stores multiple AI models for different environments, then the terminal can adapt to changing environments, but the device complexity increases
Solution Approach 1:
The patent segments the AI model system into two independent parts: model identity information (stored and managed in the network or cloud) and actual model parameters (stored locally at the terminal). This segmentation allows the terminal to maintain multiple models without increasing local complexity, as the network manages the overall model portfolio and the terminal only needs to perform simple identity matching and local retrieval operations.
Solution Approach 2:
The patent implements preliminary action by pre-configuring the terminal with AI model identities and their associated environment conditions before the terminal actually needs to adapt. The model identities are pre-linked to specific environmental scenarios, so when the terminal encounters a new environment, it can directly match the current conditions with pre-configured identities and activate the corresponding model without complex real-time decision-making.
3Adaptability or versatility
If the terminal dynamically switches AI models based on environment changes, then the AI model environmental intelligence improves, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-establishing the mapping relationships between model identities and environmental conditions before runtime. The terminal stores pre-configured associations between identity information and environmental parameters, so when environmental changes occur, the terminal can perform rapid identity matching without complex real-time analysis, significantly reducing model switching time while maintaining high adaptability.
Solution Approach 2:
The patent implements local quality by optimizing the terminal's model switching mechanism to handle only lightweight identity matching operations locally, while leaving complex model management and full model data in the network. The terminal maintains local copies of only the essential identity identifiers and environmental condition thresholds, enabling fast local decision-making without the processing burden of managing complete model datasets.
Data Source
AI summary
This application discloses an AI model processing method and apparatus, and a communication device. The method includes: obtaining, by a terminal, at least one piece of AI model information, the AI model information carrying or being associated with a model identity of an AI model, where the model identity indicates, or includes, or is associated with first information; in a case that the environment or working status or operating parameter associated with the first information changes, activating, or switching, or updating, by the terminal, to obtain a target AI model; or, in a case that the terminal identifies the model identity of the target AI model, activating, or switching, or updating, by the terminal, to obtain the target AI model, where the model identity of the target AI model indicates, or includes, or is associated with the identity information of the terminal.