Multi-TRP AI/ML Model Management for Wireless Signal Efficiency
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently performing wireless signal transmission and reception procedures.
Innovation Solution
The implementation of artificial intelligence/machine learning (AI/ML) models for managing multiple transmissions and receptions (TRPs) by monitoring and adjusting these models based on performance metrics derived from multi-TRP data sets, ensuring efficient signal transmission and reception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI/ML models are used for managing multiple TRPs, then wireless signal transmission and reception efficiency is improved, but device complexity increases
Solution Approach 1:
The terminal autonomously monitors AI/ML model performance using multi-TRP data sets and automatically triggers model updates when performance degrades below thresholds, eliminating the need for network-controlled model management and reducing signaling overhead
Solution Approach 2:
The system dynamically adjusts AI/ML model parameters based on monitored performance metrics and data distribution characteristics from multiple TRPs, optimizing model behavior for different wireless conditions without requiring complete model retraining
2Productivity
If AI/ML model management is implemented with performance monitoring, then communication process optimization is improved, but loss of time increases
Solution Approach 1:
The terminal continuously monitors AI/ML model performance using second multi-TRP data sets and compares performance metrics against thresholds, automatically triggering model updates only when degradation is detected, thereby optimizing communication processes without constant intervention
Solution Approach 2:
The system performs partial model updates based on performance monitoring results rather than continuous full model retraining, updating only when necessary to maintain acceptable performance levels, thus reducing time overhead
Data Source
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AI summary
A method performed by a terminal in a wireless communication system according to at least one of the embodiments disclosed herein may include configuring at least one artificial intelligence/machine learning (AI/ML) model related to multiple transmissions and receptions (TRPs), monitoring the at least one AI/ML model, and performing, based on a performance of the monitored at least one AI/ML model, AI/ML model management to maintain or at least partially change the at least one AI/ML model, wherein the performance of the at least one AI/ML model may be determined based on a first multi-TRP data set related to training of the at least one AI/ML model and a second multi-TRP data set related to monitoring of the at least one AI/ML model.