Wireless Communication AI Model Identity Management
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Solution Overview
Problem
The increasing complexity of wireless communication systems due to the use of multiple AI models for different problems, entities, and environments necessitates a method to effectively instruct and manage these models for improved accuracy and efficiency.
Innovation Solution
A method involving a first device that determines first information, such as a public or local identity associated with a model, to facilitate the identification and management of AI models in wireless communication systems, allowing for optimized deployment and usage based on regional or organizational scopes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple AI models are used for different problems, entities, and environments, then the accuracy of results is improved, but the device complexity increases
Solution Approach 1:
The patent segments the model management function by introducing separate identification mechanisms (first information including public identity and local identity) to distinguish between different AI models. This allows the system to manage multiple models without overwhelming complexity, as each model is tagged with specific identifiers that enable organized retrieval and execution.
Solution Approach 2:
The patent introduces an intermediary information structure (the first information containing public identity and local identity) that mediates between the complexity of having multiple models and the need for manageable system architecture. This intermediary layer enables the system to reference multiple models without directly managing their full complexity.
2Adaptability or versatility
If different models are used for different execution entities and environments, then the adaptability is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent applies local quality by introducing a local identity component that is specific to each execution entity or environment. This allows the system to maintain adaptability across different contexts while making model identification easier, as each local entity has its own distinctive identifier that simplifies detection and measurement within that specific context.
Solution Approach 2:
The patent segments model identification into two parts: a public identity that is common across all models and a local identity that is specific to each execution entity. This segmentation makes it easier to detect and measure models in different environments, as the local identity provides immediate context-specific identification without requiring analysis of the entire model structure.
3Productivity
If the number of models is increased to ensure accuracy, then the productivity is improved, but the device complexity increases
Solution Approach 1:
The patent uses copying by creating simplified reference copies of models through the first information structure (public identity and local identity). Instead of managing the full complexity of each model, the system manages these lightweight copies that point to the actual models, enabling efficient deployment of multiple models without proportional increase in management complexity.
Data Source
AI summary
Provided in the present application is a first device, including one or more processors, and one or more memories for storing a program executable by the one or more processors, where the one or more processors are configured to determine first information, wherein the first information includes one or more of the following: a first public identity, associated with a first model for wireless communication; or a first local identity, associated with the first model and/or the first public identity. Also provided are a first device and a second device.