AI/ML Model Timing Control in Mobile Communication Equipment
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Solution Overview
Problem
Existing mobile communication systems face inefficiencies in communication control due to the lack of integration of advanced AI/ML technologies, particularly in managing and executing operations related to AI/ML models, leading to suboptimal performance in areas such as CSI feedback, beam management, and positioning accuracy.
Innovation Solution
A communication control method and user equipment that utilize a model management entity to transmit timing information for executing operations like model training and updating AI/ML models, enabling efficient management and execution of AI/ML operations in user equipment and base stations, thereby enhancing communication efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI/ML model training and management operations are performed continuously without timing control, then model accuracy and communication efficiency are improved, but system overhead and resource consumption increase
Solution Approach 1:
The patent implements periodic action by having the model training entity execute model training operations at specific timing intervals determined by the model management entity. The timing information controls when model training history information is transmitted, when models are updated, and when model identification information is refreshed, converting continuous operations into periodic, controlled actions that reduce overhead while maintaining effectiveness
Solution Approach 2:
The model management entity determines and transmits timing information in advance to the model training entity before the actual model training operations are executed. This preliminary action allows the system to prepare and coordinate resources ahead of time, enabling efficient execution of model training, history transmission, and updates without last-minute rushes or resource conflicts
2Measurement precision
If model training history information is transmitted frequently to the model management entity, then model management accuracy is improved, but communication overhead and network resource usage increase
Solution Approach 1:
The transmission of model training history information from the model training entity to the model management entity is controlled to occur only at specific timing intervals. This periodic transmission approach ensures that the model management entity receives updated information regularly enough to maintain accurate model management, while avoiding excessive transmissions that would waste network resources and energy
3Ease of operation
If AI/ML operations are executed without centralized timing control, then system autonomy and responsiveness are improved, but coordination efficiency and resource utilization deteriorate
Solution Approach 1:
The model management entity serves as an intermediary that receives timing information from the network management entity and distributes it to model training entities. This intermediary structure maintains system autonomy by allowing distributed model training operations while improving coordination efficiency through centralized timing control, ensuring that all entities operate in a coordinated manner without requiring direct peer-to-peer communication
Data Source
AI summary
A communication control method according to an aspect is a communication control method in a mobile communication system. The communication control method includes transmitting, by a model management entity, timing information indicating an execution timing of a predetermined operation to a model training entity. Further, the communication control method includes executing, by the model training entity, the predetermined operation at the execution timing. Here, the predetermined operation is at least one selected from the group consisting of transmitting model training history information indicating a history of model training performed on a first AI/ML model to the model management entity, transmitting a second AI/ML model after model training has been performed on the first AI/ML model to the model management entity, and updating model identification information identifying the second AI/ML model.