Idle UE Beam Learning for Low-Overhead 5G Beam Management
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Solution Overview
Problem
Existing AI-powered beam management systems in 5G NR networks face inefficiencies due to the assumption that the beam management model remains well-trained, which is challenged by dynamic factors like interference, UE mobility, and signal strength variations, leading to high signaling overhead and performance degradation.
Innovation Solution
Implement a delivery beam management learning model trained by inputting beam parameter measurements from user equipment, allowing for adaptive beam determination and reducing the number of beams required for signal measurement and reporting, thereby optimizing beam management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If AI-powered beam management systems assume the beam management model remains well-trained, then system complexity is reduced, but performance degrades due to dynamic factors like interference, UE mobility, and signal strength variations
Solution Approach 1:
The patent implements dynamic beam management by continuously adapting the beam selection process based on real-time measurements from multiple UEs. The system transitions from static pre-trained models to dynamic model updates using aggregated measurement data, allowing the beam management system to adapt to changing network conditions while maintaining manageable complexity through structured data collection and processing protocols
Solution Approach 2:
The system establishes a feedback loop where measurement data from UEs is collected, aggregated, and used to update the beam management learning model. This continuous feedback mechanism ensures the model remains accurate despite dynamic network conditions, resolving the contradiction between model simplicity and performance reliability by making the system self-correcting through measured performance data
2Measurement precision
If traditional beam management methods are used with multiple beams for measurement, then measurement accuracy is improved, but signaling overhead increases
Solution Approach 1:
The patent merges measurement activities across multiple UEs by aggregating their measurement data for the same set of beams. Instead of each UE independently reporting measurements that would require separate signaling, the system combines measurements from multiple UEs, reducing redundant signaling while maintaining measurement precision through diversified sampling across different spatial locations and conditions
Solution Approach 2:
The system uses measurements from multiple UEs (excessive action) to compensate for the reduced number of beams measured by each individual UE. By collecting partial measurements from multiple sources, the system achieves comprehensive coverage and high measurement accuracy without requiring each UE to measure all beams, thereby reducing individual UE signaling overhead while maintaining overall measurement precision
Data Source
AI summary
A radio network node transmits beam reporting configuration information indicative of measurement beams. User equipment receive the information and measure signals transmitted via the measurement beams. The user equipment transmits measured measurement beam signal values to the node via an existing connection, or if the user equipment is idle, via a special-purpose connection established in response to a request by the user equipment. An idle user equipment may avoid requesting a special-purpose connection if a difference between a measured measurement beam signal value and a measured synchronization signal block signal value does not exceed a reporting criterion. The node may analyze measured signal values received from the user equipment using a learning model to determine a refined delivery beam usable to deliver traffic to the user equipment, and may analyze a measured signal value, reported by the user equipment, corresponding to the delivery beam to determine a different delivery beam.