AI/ML Signal Processing Model Setup for Cross-Vendor Compatibility
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Solution Overview
Problem
Signal processing between AI/ML models of different terminal apparatuses and a base station may fail due to differences in specifications or implementations, leading to improper communication.
Innovation Solution
A communication apparatus with a signal processor, receiver, and controller that sets an AI/ML model based on received learning information, and a data set providing apparatus that shares common learning information across different vendor communication apparatuses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI/ML models with different specifications and implementations are used in terminal apparatuses and base stations, then each apparatus can have optimized performance for its specific implementation, but signal processing between them will not be performed properly due to incompatibility
Solution Approach 1:
The patent changes the parameters of AI/ML models by introducing standardized configuration parameters that define model structure, input/output formats, and processing characteristics. These parameters enable different implementations to be adjusted to a common standard, ensuring compatibility while allowing optimization within parameter constraints.
Solution Approach 2:
The patent creates a universal AI/ML model framework that can accommodate multiple different implementations and specifications. The standardized interface and configuration system allow a single framework to serve multiple functions across different vendor implementations, ensuring interoperability while maintaining implementation flexibility.
2Ease of manufacture
If each terminal apparatus uses its own AI/ML model implementation, then vendor-specific optimizations can be achieved, but communication between terminal apparatuses and base stations will fail due to specification differences
Solution Approach 1:
The patent introduces an intermediary standardized configuration system that mediates between vendor-specific AI/ML model implementations and the required interoperability. This intermediary layer translates and harmonizes different vendor specifications into a common format that ensures seamless communication while preserving vendor optimization capabilities.
Solution Approach 2:
The patent segments the AI/ML model implementation into distinct configurable components and parameters. This segmentation allows vendor-specific optimizations in individual components while maintaining standardized interfaces and parameter definitions that ensure overall system interoperability and ease of operation.
Data Source
AI summary
[Object] To provide a communication apparatus, a data set providing apparatus, a method for training an AI/ML model, and a method for providing information on which to base learning of an AI/ML model that make it possible to perform signal processing without being aware of a difference in AI/ML model.[Solving Means] A communication apparatus includes a signal processor that includes an AI/ML model; a receiver that receives, from another communication apparatus, information on which to base learning; and a controller that sets the AI/ML model on the basis of the information on which to base learning.