UE-Side AI/ML Beam Management With Condition ID Validation
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Solution Overview
Problem
Existing wireless communication systems face challenges in maintaining consistency and performance of UE-sided AI/ML models due to discrepancies between training and inference conditions, particularly in beam management, where differences in beam codebooks and indexing degrade model performance, and there is a lack of clear procedures for ID assignment and data acquisition.
Innovation Solution
Implementing detailed signaling and procedural frameworks for data collection, including the use of condition IDs to manage UE data collection, validate NW-side conditions, and ensure model consistency, with mechanisms like refresh timers, validity tags, and timestamps to maintain model relevance during handovers.
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 model performance degrades when there is a discrepancy between training and inference conditions such as differences in beam codebooks and beam indexing
Solution Approach 1:
The patent applies preliminary action by training UE-sided AI/ML models in advance using data measured from specific network configurations. The models are pre-trained with knowledge of particular beam codebooks, beam indexing schemes, and other NW configuration parameters. This allows the models to maintain high performance when the inference conditions match the training conditions, while the system handles configuration mismatches through explicit condition tracking and validation mechanisms.
2Loss of information
If NW-side additional conditions are provided implicitly through an associated ID, then signaling overhead is reduced, but procedures for ID assignment, validation, and data acquisition become undefined
Solution Approach 1:
The patent uses an associated ID as an intermediary element that implicitly represents NW-side additional conditions. Instead of transmitting full configuration details, the system uses compact ID identifiers that reference pre-defined condition sets. This reduces signaling overhead while maintaining procedural clarity through explicitly defined procedures for ID assignment, validation, and data acquisition that operate on these intermediary identifiers.
3Stability of the object's composition
If data collection is performed during handovers, then model consistency is maintained, but complexity of managing AI/ML model training increases
Solution Approach 1:
The patent applies preliminary action by preparing and validating AI/ML model configurations before handover events occur. The system pre-configures condition IDs, validates NW-side conditions, and prepares data collection parameters in advance. During handover, this pre-prepared information enables seamless model consistency maintenance without requiring complex real-time decision-making, thereby reducing operational complexity while preserving model stability.
4Manufacturing precision
If detailed procedures are defined for ID assignment and data acquisition, then model training accuracy is improved, but signaling complexity increases
Solution Approach 1:
The patent segments the complex model training process into distinct, manageable phases with clearly defined procedures. ID assignment, condition validation, and data acquisition are separated into independent procedural steps, each with specific inputs and outputs. This segmentation improves model training accuracy by ensuring each step is executed correctly while reducing overall signaling complexity through modular, standardized procedures that can be independently implemented and validated.
Data Source
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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.