UE Beam Management Using Motion Sensor Data
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Solution Overview
Problem
Current beam management procedures for mmWave channels in 5G NR technology are inefficient due to high training time and resource usage, particularly in scenarios with environmental changes and user equipment (UE) mobility, requiring extensive search mechanisms for optimal beam pairs.
Innovation Solution
The implementation of UE-based beam management systems that utilize prior knowledge of position and rotation angles from motion sensors to adjust beamforming without new measurements, allowing for efficient beam updating and prediction of channel changes with acceptable mean square error performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive beam sweeping and search mechanisms are used to find optimal beam pairs, then beam management reliability is improved, but training time and resource overhead increase significantly
Solution Approach 1:
The system performs preliminary beam pairing and channel estimation during initial access or previous transmission time intervals. The UE and gNB pre-establish beam pairs and store channel state information, so that when channel changes occur, the system can quickly update beams using pre-computed information rather than performing exhaustive searches, thus reducing training time while maintaining reliability
Solution Approach 2:
The system copies and reuses previously established beam pairs and channel estimation results for similar channel conditions. When the UE is stationary or experiences minimal movement, the system can copy existing beam configurations instead of performing new beam sweeping, significantly reducing training time while maintaining beam management reliability through periodic validation
2Measurement precision
If exhaustive beam sweeping procedures are implemented for every beam management event, then beam direction accuracy is improved, but resource overhead and network signaling increase
Solution Approach 1:
Beam pairs and channel estimates are pre-determined during initial access or previous transmission intervals. This preliminary action creates a repository of beam pairing information that can be quickly referenced and updated, eliminating the need for exhaustive beam sweeping in every management event while maintaining accurate beam direction through selective re-evaluation
Solution Approach 2:
The system changes the operational parameters of beam management by transitioning from exhaustive beam sweeping to incremental updates based on UE movement detection. When movement is detected, the system updates beam parameters using pre-stored channel information and movement vectors, achieving accurate beam direction with reduced resource overhead compared to full beam sweeping
3Measurement precision
If new beam measurements and sweeping are performed for every UE movement, then beam tracking accuracy is improved, but beam management complexity increases
Solution Approach 1:
The UE autonomously detects its own movement using motion sensors (accelerometers, gyroscopes) and self-corrects beamforming weights based on pre-stored channel information and movement data. This self-service mechanism eliminates the need for complex network-coordinated beam management procedures, reducing overall system complexity while maintaining accurate beam tracking through UE-initiated corrections
Solution Approach 2:
The system replaces mechanical beam sweeping and measurement procedures with computational beam correction based on sensor data and pre-stored channel models. Instead of physically sweeping beams across all possible directions, the system uses movement vectors and channel state information to computationally determine the optimal beam direction, significantly reducing management complexity while maintaining tracking accuracy
Data Source
AI summary
Methods and apparatuses are provided in which position information is determined corresponding to movement of a user equipment (UE) from first local coordinates to second local coordinates. Receive angles of the UE are derived from the position information of the UE. A beamforming weight of the UE is determined based on the derived receive angles of the UE. The beamforming weight is configured such that a beam direction of the second local coordinates matches a beam direction of the first local coordinates.


