Spatial Beam Quality Prediction for Low-Overhead RSRP Reporting
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Solution Overview
Problem
Traditional beam management procedures in wireless communication involve transmitting and measuring all beams, which is inefficient and leads to high overhead and latency, while AI/ML-based methods can improve beam prediction and selection accuracy but require optimized reference signal transmission strategies.
Innovation Solution
A wireless transceiver unit (WTRU) uses AI/ML models to estimate beam qualities based on measurements of selected beams, determining and reporting beam qualities using different criteria for subsets of beams, including conditions such as SINR, CQI, noise power, and LOS probability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all beams are transmitted and measured to identify the best beam, then beam selection accuracy is improved, but overhead and latency increase
Solution Approach 1:
The system performs preliminary beam quality estimation using AI/ML models before actual beam selection. The model predicts beam qualities based on historical data and spatial relationships, allowing the system to pre-identify potential best beams without measuring all beams, thus reducing measurement time and latency while maintaining selection accuracy.
Solution Approach 2:
Instead of directly measuring all beam qualities, the system uses AI/ML models to create a virtual copy or prediction of beam qualities based on measurements from a subset of beams. This predicted beam quality information is then used for beam selection, avoiding the need to physically measure all beams and reducing overhead and latency.
2Measurement precision
If all beams are transmitted and measured to identify the best beam, then beam selection accuracy is improved, but overhead increases
Solution Approach 1:
The system applies partial action by measuring only a subset of beams rather than all beams. The AI/ML model then infers the qualities of unmeasured beams based on the measured subset, reducing the quantity of measurements and reporting overhead while maintaining sufficient accuracy for beam selection.
Solution Approach 2:
The AI/ML model creates a predicted copy of beam quality information for beams that were not directly measured. This virtual copying approach reduces the actual measurement overhead while providing complete beam quality information for selection purposes.
3Quantity of substance
If AI/ML models are used to estimate beam qualities, then measurement overhead is reduced, but measurement precision may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the AI/ML model continuously learns from actual beam measurements and selection outcomes. This feedback loop allows the model to refine its predictions and improve accuracy over time, compensating for the reduced measurement overhead while maintaining or enhancing beam quality estimation precision.
Solution Approach 2:
The system dynamically adjusts the balance between measured and predicted beam qualities based on changing conditions such as mobility, channel characteristics, and historical accuracy. By changing parameters like the subset of beams to measure or the model complexity, the system optimizes the trade-off between overhead reduction and precision maintenance under different operational scenarios.
4Adaptability or versatility
If different criteria are used for different beam subsets, then beam management flexibility is improved, but system complexity increases
Solution Approach 1:
The system applies local quality by using different measurement and reporting criteria for different beam subsets based on their specific characteristics. For example, highly directional beams may use different evaluation criteria compared to omnidirectional beams. The AI/ML model handles these差异化 criteria automatically, providing flexibility while managing complexity through intelligent processing rather than explicit complex control logic.
Data Source
AI summary
A wireless transceiver/receiver unit (WTRU) comprises a processor configured to receive configuration information that may include an indication of a first and second set of beams, where the second set of beams may be a subset of the first set of beams. The configuration may include an indication of a first set of reference signals (RSs) resources associated with the first set of beams and an indication of a second set of RS resources associated with the second set of beams. The processor may determine a measured beam quality associated with each of the beams of the second set of beams based on measurements performed on the second set of RS resources, determine a predicted measurement associated with each of the beams of the first set of beams based on the measured beam quality for the one or more beams of the second set of beams, and send a report.