UE Beam Measurement Prediction for Adaptive 5G Beam Selection
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Solution Overview
Problem
Existing beam management systems in cellular networks, such as 5G, face challenges in efficiently selecting and maintaining optimal antenna beams due to variations in signal quality and environmental conditions, leading to suboptimal performance and inefficiencies in spectral efficiency and coverage.
Innovation Solution
Implementing an AI-ML model on user equipment (UE) to predict beam measurement qualities, allowing for differentiation between actual and predicted beam measurements, and incorporating a life cycle management (LCM) procedure to adjust the model based on performance thresholds, ensuring the selection of the best beams for communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beam management systems rely solely on actual beam measurements, then measurement accuracy is maintained, but spectral efficiency and coverage deteriorate due to inability to adapt to changing environmental conditions
Solution Approach 1:
The system performs preliminary beam measurements and uses AI-ML models to predict future beam qualities before actual measurements are taken. This allows the network to proactively identify and switch to optimal beams before signal degradation occurs, improving spectral efficiency while maintaining measurement accuracy through the combination of predicted and actual measurements.
Solution Approach 2:
The system implements a feedback mechanism where actual beam measurements are continuously compared with AI-ML model predictions. This feedback loop allows the system to validate predictions against real measurements, refine the AI-ML models, and dynamically adjust beam selection strategies to optimize both measurement accuracy and spectral efficiency under varying environmental conditions.
2Device complexity
If beam management systems use traditional measurement methods, then system complexity is minimized, but coverage and spectral efficiency worsen due to inability to dynamically adapt to environmental variations
Solution Approach 1:
The system introduces AI-ML models as an intermediary layer between traditional beam measurement methods and beam selection decisions. These models process actual measurements and generate predictions about future beam qualities, enabling dynamic adaptation to environmental changes without requiring complete redesign of the beam management system, thus balancing complexity and adaptability.
Solution Approach 2:
The system dynamically changes key parameters such as beam measurement thresholds, prediction confidence levels, and beam switching criteria based on environmental conditions. By adjusting these parameters rather than restructuring the entire system, the patent achieves environmental adaptability while maintaining relatively simple system architecture.
3Productivity
If AI-ML models are implemented for beam prediction, then spectral efficiency and coverage improve through predictive capabilities, but device complexity increases due to model execution requirements
Solution Approach 1:
The system segments the AI-ML processing tasks by separating model training (performed on the network side with abundant computational resources) from model execution (performed on the UE with simpler requirements). This segmentation allows complex predictive models to improve spectral efficiency while minimizing the processing burden on user equipment through optimized, pre-trained model deployment.
4Measurement precision
If frequent beam measurements are performed to maintain accuracy, then measurement precision is improved, but loss of time and energy increases
Solution Approach 1:
The system performs preliminary beam measurements and uses AI-ML models to predict future beam qualities, reducing the need for frequent actual measurements. By predicting beam qualities in advance, the system maintains measurement precision while significantly reducing the time and energy required for continuous measurements, as predictions can be generated without performing full measurement sequences.
Data Source
AI summary
The present application relates to devices and components including apparatus, systems, and methods to process beam measurement predictions associated with beams. In an example, a network configures a UE with different configurations: one for measuring reference signals on a first set of beams, and one for performing beam measurement predictions for a second set of beams. Upon receiving reference signals on the first set of beams, the UE can generate beam measurements. The UE can also execute an AI model that outputs the beam measurement predictions based on an input that includes the beam measurements. The UE can be further configured to report the beam measurement predictions and/or to determine, based on such predictions, particular beams on which additional reference signals are to be received. In the latter case, upon receiving reference signals, the UE can generate and report the corresponding beam measurements.