Predictive Beam Refinement for Low-Latency NR Beam Selection
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Solution Overview
Problem
Beam management in NR systems consumes significant resources and causes latency due to beam sweeping and measurement operations, especially at high frequencies, which can be improved through AI/ML-based predictive beam refinement.
Innovation Solution
Implementing AI/ML models at WTRUs and network nodes for predictive beam refinement, using configuration information to train models based on beam sweeping, identify optimal beams, and adjust thresholds for measurement storage and model retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beam sweeping and measurement operations are performed to identify suitable beams, then beam selection accuracy is improved, but resource consumption and latency increase
Solution Approach 1:
The system performs preliminary beam sweeping and stores measurement results in buffers before actual beam selection is needed. Training data samples are collected and stored in advance, allowing the AI/ML model to make quick predictions without performing full beam sweeping at the moment of decision, thus reducing latency while maintaining accuracy
Solution Approach 2:
Instead of performing actual beam sweeping operations repeatedly, the system uses stored training data samples that replicate the measurement information. The AI/ML model processes these copied data representations to predict beam quality, avoiding the time-consuming physical measurement process while preserving the essential selection criteria
2Adaptability or versatility
If beam sweeping is performed across many beams at high frequencies, then comprehensive beam coverage is achieved, but resource consumption increases
Solution Approach 1:
The AI/ML model serves multiple functions: it predicts beam quality, determines when retraining is needed, manages buffer storage, and handles threshold comparisons. This multi-functional approach consolidates what would otherwise require separate processing systems, reducing overall resource consumption while maintaining comprehensive beam coverage capability
Solution Approach 2:
The system dynamically changes parameters such as buffer size thresholds and retraining triggers based on stored measurement data. By adjusting these parameters adaptively rather than using fixed conservative values, the system achieves comprehensive beam coverage only when necessary, optimizing resource usage while maintaining versatility
3Measurement precision
If AI/ML models are trained with extensive beam measurement data, then prediction accuracy is improved, but training complexity and overhead increase
Solution Approach 1:
The system uses a buffer size threshold to determine when enough training data has been collected. Rather than requiring exhaustive data collection, the system proceeds with training when the buffer reaches a sufficient threshold level, achieving good prediction accuracy without the excessive complexity of complete data set preparation
Solution Approach 2:
The system implements feedback mechanisms where the AI/ML model's performance and the buffer status inform when retraining should occur. This feedback loop allows the system to maintain accuracy by retraining only when necessary, rather than continuously, thus reducing training complexity and overhead while preserving prediction quality
Data Source
AI summary
A wireless transmit/receive unit (WTRU) may be configured to receive a configuration for predictive beam refinement. The configuration may include at least one threshold. The at least one threshold may be related to information. The information may be used to predict and/or select one or more beam-pairs. The WTRU may perform measurements on one or more reference signal (RS) sets. The WTRU may determine to store one or more of the measurements, for example as training data samples in a memory. The WTRU may determine to store the one or more measurements based on a comparison of the one or more measurements against the at least one threshold. The WTRU may determine to begin training or retraining a model for performing a prediction related to the one or more beam pairs. The WTRU may transmit an indication that the stored training data samples have reached the at least one threshold.