UE Beam Management AI/ML Configuration With Condition ID Validation
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Solution Overview
Problem
Existing wireless communication systems face challenges in maintaining consistency between AI/ML model training and inference due to discrepancies in beam codebooks and indexing across different cells, leading to degraded performance, with previous solutions lacking clear procedures for ID assignment, validation, and data acquisition.
Innovation Solution
Implementing improved signaling and procedures for data collection, including the use of condition IDs to manage UE data collection, ensure model consistency, and maintain performance during handovers, through mechanisms like refresh timers, validity tags, and timestamps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If UE-sided AI/ML models are trained using data measured from specific NW configurations, then model performance is improved, but discrepancies between training and inference conditions (such as beam codebooks and beam indexing) degrade model performance
Solution Approach 1:
The system performs preliminary actions by collecting and storing NW configuration data (beam codebooks, beam indexing, resource configurations) during the training phase, and then validates these configurations during inference. The UE stores training configuration data and compares it with current network configurations to ensure consistency, thereby maintaining model performance across different cells and conditions.
Solution Approach 2:
The system changes parameters by dynamically adjusting the validation process based on configuration mismatches. When discrepancies are detected between stored training configurations and current network configurations (such as beam codebook differences or indexing changes), the system triggers re-validation or re-training procedures to adapt the model to new parameter sets, ensuring continued reliability.
2Loss of information
If NW-side additional conditions are provided implicitly through an associated ID, then signaling overhead is reduced, but clear procedures for ID assignment, validation, and data acquisition are not defined
Solution Approach 1:
The system segments the configuration validation process into distinct phases: ID assignment phase, data collection phase, and validation phase. Each phase has clearly defined procedures and responsibilities. The UE and gNB follow structured workflows for each segment, making the overall complex process manageable and clear while maintaining efficient signaling through the use of compact identification fields.
Solution Approach 2:
The condition ID serves as an intermediary that bridges the gap between detailed NW configurations and compact signaling. Instead of transmitting full configuration data, the system uses condition IDs as mediators that reference pre-defined configuration sets. This intermediary approach reduces signaling overhead while the detailed procedures for ID assignment and validation ensure operational clarity.
3Stability of the object's composition
If data collection is performed during handovers, then model consistency is maintained, but additional signaling and processing requirements increase system complexity
Solution Approach 1:
The system performs preliminary actions by pre-configuring the UE with model consistency validation procedures before handover occurs. The UE is prepared with the necessary configuration data and validation logic in advance, so that during handover, it can efficiently compare pre-handover and post-handover configurations without adding significant processing complexity. This preliminary preparation maintains model consistency while minimizing handover complexity.
Data Source
AI summary
A system and a method performed by a UE in a wireless communication system includes receiving, from a base station, a message corresponding to a data collection request by the UE; transmitting, to the base station, the data collection request with, at least one of, a preferred configuration or time interval; receiving, from the base station, a response to enable UE data collection including a resource configuration and a condition identifier in response to the data collection request; and performing data collection by measuring the resource configuration.