Dynamic UE Positioning Switching for NLOS and High-Doppler Conditions
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Solution Overview
Problem
Existing positioning technologies in 5G NR networks face challenges in accurately determining user equipment (UE) location, particularly in non-line-of-sight (NLOS) conditions and high Doppler environments, where traditional methods struggle with overhead and latency.
Innovation Solution
Integration of AI-based positioning schemes with traditional positioning techniques, allowing for dynamic switching between methods based on environmental conditions, calibration using traditional techniques to update AI models, and hybrid use of AI and traditional methods to enhance accuracy and reduce overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional positioning methods are used, then the positioning can be performed with existing infrastructure, but the positioning accuracy deteriorates in NLOS conditions and high Doppler environments
Solution Approach 1:
The system dynamically selects between traditional positioning methods and AI-based positioning methods based on environmental conditions such as NLOS detection and Doppler shift levels. The positioning method is not fixed but adapts in real-time to maintain accuracy across varying conditions.
Solution Approach 2:
The system changes the positioning approach parameter based on detected environmental conditions. When NLOS or high Doppler conditions are detected, the system transitions from traditional positioning to AI-based positioning, effectively changing the operational parameter to maintain reliability.
2Measurement precision
If AI-based positioning schemes are used, then positioning accuracy improves in challenging environments, but the system complexity and computational overhead increase
Solution Approach 1:
The positioning system is segmented into two distinct pathways: traditional positioning for normal conditions and AI-based positioning for challenging conditions. This segmentation allows the system to use complex AI methods only when necessary, reducing overall computational overhead while maintaining high accuracy when needed.
Solution Approach 2:
An intermediary mechanism detects environmental conditions (NLOS, Doppler) and mediates the selection between traditional and AI-based positioning methods. This intermediary layer manages the complexity by automatically determining when AI processing is required, reducing the burden on the system.
3Measurement precision
If AI models are continuously updated with traditional positioning data, then positioning accuracy improves over time, but the calibration overhead and processing time increase
Solution Approach 1:
The AI model calibration is performed periodically using traditional positioning data rather than continuously. This periodic updating approach allows the system to maintain improved positioning accuracy over time while minimizing the time and computational resources dedicated to calibration activities.
Data Source
AI summary
A user equipment (UE) configured to determine the UE to be capable of performing a first positioning scheme, determine the UE to be capable of performing a second positioning scheme, select one of the first and second positioning schemes the UE is to use to perform a positioning operation and calculate a position of the UE using the one of the first and second positioning schemes.