Communication Apparatus Machine Learning Capability Signaling
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Solution Overview
Problem
There is a lack of established techniques for effectively leveraging machine learning processing in mobile communication systems.
Innovation Solution
A communication apparatus and method that utilize machine learning processing to derive learned models and infer results, with the ability to transmit messages containing information about processing and storage capacities to other communication apparatuses in the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning processing is applied in mobile communication systems, then productivity and communication efficiency are improved, but device complexity increases due to the need for processing and storage capacity
Solution Approach 1:
The patent introduces capability information as an intermediary element that mediates between the machine learning processing requirements and the communication system. This capability information, transmitted through signaling messages, allows the system to negotiate and match ML processing capabilities without requiring all devices to have high processing capacity, thus resolving the contradiction between improved productivity and device complexity
Solution Approach 2:
The patent changes the parameter representation by introducing capability information that quantifies processing and storage capacities. This allows dynamic adjustment of ML processing parameters based on actual device capabilities, enabling the system to optimize productivity while adapting to varying device complexity levels
2Adaptability or versatility
If machine learning models are transmitted between communication apparatuses, then adaptability is improved, but loss of information increases due to overhead signaling requirements
Solution Approach 1:
The patent extracts the essential capability information from the full machine learning model and transmits only this extracted information through signaling messages. This allows the system to achieve adaptability by sharing model capabilities without transmitting the entire model, thereby reducing information loss from overhead signaling while maintaining the ability to adapt to different communication scenarios
3Manufacturing precision
If processing capacity is increased to support machine learning, then manufacturing precision of communication performance is improved, but use of energy increases
Solution Approach 1:
The patent implements dynamic capability information that allows the communication apparatus to adjust its machine learning processing based on real-time conditions and capability negotiations. This dynamic approach enables the system to achieve precise communication performance when needed while reducing energy consumption by utilizing only the necessary processing capacity for each specific communication task, rather than maintaining constant high processing power
Data Source
AI summary
A communication apparatus configured to communicate with a communication apparatus in a mobile communication system using a machine learning technology includes a controller configured to perform machine learning processing of learning processing to derive a learned model by using learning data and/or inference processing to infer inference result data from inference data by using the learned model, and a transmitter configured to transmit, to the communication apparatus, a message including an information element related to a processing capacity and/or a storage capacity usable by the communication apparatus for the machine learning processing.