AI/ML Beam Management with Preconfigured Reference Signal Sets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing beam management methods in NR are inefficient and resource-intensive, particularly in managing beam sets and determining appropriate reference signals, leading to increased power consumption and latency in UE operations.
Innovation Solution
Implementing artificial intelligence and machine learning (AI/ML) for model management in beam management by preconfiguring reference signal resource sets and dynamically transmitting/receiving appropriate reference signals based on UE context, allowing faster beam set measurement and indication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional beam management methods are used, then beam sets can be managed, but power consumption and latency increase
Solution Approach 1:
The base station preconfigures multiple reference signal resource sets corresponding to different AI/ML models before UE operations. This preliminary preparation allows UEs to directly use these pre-configured resources for beam management without performing additional information exchange, thereby reducing both power consumption and latency in beam management operations
2Measurement precision
If additional information exchange is performed for beam set determination, then accurate beam selection is achieved, but latency increases
Solution Approach 1:
The base station preconfigures multiple reference signal resource sets corresponding to different AI/ML models before UE operations. This preliminary preparation allows UEs to directly use these pre-configured resources for beam management without performing additional information exchange, thereby reducing both power consumption and latency in beam management operations
Solution Approach 2:
AI/ML models serve as intermediaries between the base station's beam configuration and UE beam selection. These models process beam management data and provide optimized beam set recommendations, enabling accurate beam selection while minimizing the need for additional information exchange between base station and UE
3Adaptability or versatility
If multiple reference signal resource sets are configured for different AI/ML models, then model management flexibility is improved, but device complexity increases
Solution Approach 1:
The base station implements a universal reference signal resource set configuration framework that can accommodate multiple AI/ML models with different requirements. Each resource set is designed to be multi-functional, supporting various beam management scenarios through a unified configuration approach, thereby maintaining flexibility while controlling complexity
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Provided are a method and device for managing a model in beam management using artificial intelligence and machine learning. The method may include: receiving configuration information about one or more reference signal (RS) resource sets configured to respectively correspond to one or more AI/ML models or functionalities used for a certain purpose; receiving instruction information about selected one RS resource set, which is selected from the one or more RS resource sets; and measuring signal intensity or signal quality of a reference signal based on the selected one RS resource set.