AI Beam Training Termination for Lower 5G System Overhead
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently managing beam training for terminals, leading to unnecessary extended training periods and increased system overhead, particularly in 5G and beyond systems with high mobility and dynamic environments.
Innovation Solution
Implementing AI/ML model training for beam management by defining methods for data collection and termination, including message exchanges between terminals and base stations, utilizing threshold and time information to optimize training duration and reduce unnecessary operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI/ML model training is performed continuously without termination conditions, then model accuracy may be improved, but system overhead and training duration increase unnecessarily
Solution Approach 1:
The patent applies preliminary action by pre-defining termination conditions (threshold information and time information) before model training begins. The base station configures these conditions in advance, allowing the terminal to automatically terminate training when conditions are met, preventing unnecessary extended training while ensuring sufficient accuracy is achieved.
Solution Approach 2:
The patent implements feedback mechanisms where the terminal monitors training progress against pre-configured threshold information (model performance thresholds) and time information (maximum training duration). When training results meet the threshold or time expires, the terminal sends termination indication messages to the base station, creating a closed-loop control system that optimizes training duration based on actual performance.
2Reliability
If data transmission for model training is maintained indefinitely, then training completeness is ensured, but system overhead increases
Solution Approach 1:
The base station pre-configures termination conditions including threshold information (performance criteria) and time information (duration limits) before training starts. This preliminary setup enables automatic termination when training objectives are achieved or time expires, ensuring training completeness while preventing indefinite data transmission and reducing system overhead.
Solution Approach 2:
The terminal autonomously monitors training progress against configured thresholds and automatically determines when to terminate data collection and transmission. This self-service mechanism eliminates the need for continuous base station monitoring and control messages, reducing system overhead while maintaining training reliability through automated decision-making.
Data Source
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AI summary
Provided are a method and apparatus for training a model for artificial intelligence and/or machine learning (AI/ML)-based communication. A terminal receives, from a base station, data for AI/ML model training, and performs AI/ML model training based on the received data. After performing the AI/ML model training, the terminal transmits, to the base station, a first message indicating termination of collection of the data.