Mobile Communication Signaling for ML Model and Data Compatibility
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Solution Overview
Problem
The integration of machine learning technology in mobile communication systems lacks established methods for determining whether a trained or untrained model, as well as training or inference data, can be utilized by reception-end entities.
Innovation Solution
A communication method is introduced where a transmission-end entity transmits first use condition information to a reception-end entity, indicating whether a training model or data set is trained or untrained, enabling the reception-end entity to determine suitability for use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning technology is integrated in mobile communication systems, then communication efficiency and intelligence are improved, but the complexity of determining model compatibility and data usability increases
Solution Approach 1:
The transmission-end entity performs preliminary action by transmitting use condition information before the actual model or data is transferred. This allows the reception-end entity to prepare appropriately and avoids unnecessary reception of incompatible data, thereby reducing overall system complexity while maintaining efficient communication.
Solution Approach 2:
Use condition information acts as an intermediary element between the transmission-end entity and reception-end entity. This intermediary carries metadata about model training status and data characteristics, enabling compatibility determination without requiring direct complex interactions between the model and receiving system.
2Reliability
If use condition information is transmitted before model data, then compatibility can be determined in advance, but transmission overhead increases
Solution Approach 1:
The invention extracts only the essential use condition information (training status and data type indicators) from the complete model data package. By taking out only these critical metadata elements for preliminary transmission, the system achieves reliable compatibility determination while minimizing unnecessary data transmission volume.
Solution Approach 2:
The transmission process is segmented into two stages: first transmitting compact use condition information for compatibility assessment, then transferring only the actual model data if compatible. This segmentation allows reliability to be improved without proportionally increasing total transmission overhead.
3Ease of operation
If training models and data sets are transmitted without use condition information, then transmission process is simplified, but the reception-end entity cannot determine suitability for use
Solution Approach 1:
The transmission-end entity performs preliminary action by including use condition information in the transmitted data package. This preliminary inclusion of metadata enables the reception-end entity to assess suitability before processing, thereby maintaining ease of operation while significantly improving adaptability.
Solution Approach 2:
The use condition information serves multiple functions simultaneously: it indicates training status, specifies data type, and enables compatibility determination. This multi-functionality approach maintains transmission simplicity while greatly enhancing the reception-end entity's adaptability to different models and data sets.
Data Source
AI summary
The present disclosure relates to a communication method in a mobile communication system. The communication method includes transmitting, by a transmission-end entity to a reception-end entity, first use condition information representing a first use condition under which a training model and/or a data set is to be used. Here, the training model is one of an untrained model that has not been trained and a trained model that has been trained. The data set is one of training data and inference data.