Beam Prediction for Lower-Layer Triggered Cell Selection
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies in mobility procedures due to high power consumption and long durations required for beam and cell switching, relying on incomplete or outdated beam metric information, which affects data throughput and reliability.
Innovation Solution
Implementing beam prediction techniques for unmeasured beams in lower-layer triggered mobility procedures, allowing UEs to initiate mobility based on predicted beam measurements and transmit reports with confidence levels, and optionally triggering aperiodic reference signals for more accurate decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional beam measurement and cell switching procedures are used, then mobility decisions are made with complete measured beam metrics, but power consumption is high and procedure duration is long
Solution Approach 1:
The system performs preliminary beam measurements and predictions in advance during idle periods or when UE is stationary. Beam metric predictions are pre-calculated using machine learning models based on historical measurement data, so that when mobility is needed, the UE can quickly access pre-computed predictions rather than performing complete real-time measurements, thereby reducing power consumption during actual mobility events while maintaining decision accuracy.
Solution Approach 2:
Instead of measuring all beam metrics in real-time, the system creates copies or predictions of beam metric values using machine learning models trained on historical measurement data. These predicted beam metrics serve as substitutes for actual measurements, allowing the UE to make mobility decisions with predicted values that replicate the information content of complete measurements without the associated measurement overhead and power consumption.
2Reliability
If complete beam measurements are performed for all candidate cells, then mobility decisions are reliable, but procedure duration is long
Solution Approach 1:
Beam metric predictions are pre-computed during idle periods or when channel conditions are stable, so that when mobility events occur, the UE can immediately use these pre-calculated predictions without waiting for complete real-time measurements. This preliminary action significantly reduces mobility procedure duration while maintaining decision reliability through the use of pre-analyzed beam quality data.
Solution Approach 2:
The system performs partial measurements combined with predictions rather than complete measurements of all beams. The UE measures only a subset of reference signals and uses machine learning models to predict metrics for remaining beams, achieving sufficient accuracy for mobility decisions with reduced measurement time and procedure duration.
3Use of energy by moving object
If beam predictions are used for unmeasured beams, then power consumption and procedure duration are reduced, but measurement precision may be insufficient
Solution Approach 1:
The system implements feedback mechanisms where actual beam measurements are continuously compared with predicted values. The UE measures a subset of reference signals and uses these actual measurements to validate and refine the machine learning model predictions. This feedback loop ensures that predictions remain accurate by continuously adjusting the model based on real measurement data, maintaining measurement precision while reducing overall power consumption.
Solution Approach 2:
The system replaces complete physical measurement processes with machine learning-based prediction mechanisms. Instead of measuring all beam metrics through actual reference signal measurements, the system uses trained ML models to predict beam quality values, substituting computational prediction for physical measurement where appropriate, thereby reducing power consumption while maintaining sufficient accuracy through model training on historical measurement data.
4Use of energy by moving object
If fewer reference signals are measured, then power consumption is reduced, but reliability of mobility decisions decreases
Solution Approach 1:
The system creates predicted copies of beam metric values for unmeasured beams using machine learning models. These predicted values serve as substitutes for actual measurements, allowing the UE to make mobility decisions with a complete set of beam metrics even though only a subset was actually measured. This copying approach maintains decision reliability by providing predicted values that replicate the information content of complete measurements without the associated power consumption.
Solution Approach 2:
The system replaces the mechanical measurement process for all beams with a hybrid approach where actual measurements of a subset are combined with ML-based predictions for remaining beams. This substitution reduces the number of physical measurements required while maintaining reliability through the use of trained prediction models that compensate for the reduced measurement set.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described that provide for a user equipment (UE) to be configured to perform beam prediction for one or more unmeasured beams of a set of beams configured for lower-layer triggered mobility (LTM). A UE may receive configuration information from a network entity that configures LTM using predicted beam measurements, and may initiate mobility from a first cell to a second cell based at least in part on the predicted beam measurements. The UE may transmit to the second cell directly to initiate mobility, or may transmit one or more beam reports to the first cell to initiate mobility. A relatively low confidence level of a prediction may trigger transmission of one or more aperiodic on-demand reference signals from a candidate cell. A UE may also transmit a capability indication of UE capabilities related to beam prediction for LTM.