UE-Based NLOS Error Mitigation via Blind Learning
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Solution Overview
Problem
Existing mobile and wireless telecommunication systems face challenges in accurately mitigating non-line of sight (NLOS) errors during positioning, which can lead to significant ranging errors and impact the reliability of location services, especially in 5G networks.
Innovation Solution
The implementation of a blind learning-type algorithm for channel bias distribution estimation, which allows user equipment (UE) to perform positioning calculations using NLOS bias distribution information received from network nodes, thereby reducing latency and improving positioning accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional positioning methods are used in NLOS conditions, then positioning can be performed, but positioning accuracy deteriorates due to NLOS errors
Solution Approach 1:
The system performs preliminary actions by having network nodes pre-calibrate and determine NLOS bias values for different spatial locations and environmental conditions. This calibration data is stored and later used to correct positioning measurements, allowing the system to proactively compensate for NLOS errors before they affect positioning accuracy
Solution Approach 2:
The system implements feedback mechanisms where positioning measurements are continuously monitored and compared against expected values. When NLOS conditions are detected, the system uses feedback loops to adjust positioning calculations by applying appropriate bias corrections, thereby maintaining positioning accuracy despite NLOS interference
2Measurement precision
If network-based positioning calculations are used, then positioning accuracy can be improved, but latency increases
Solution Approach 1:
The positioning functionality is segmented and distributed: network nodes perform calibration and provide assistance data, while user equipment performs the actual positioning calculations locally. This segmentation allows computationally intensive tasks to be distributed, reducing network latency while maintaining accuracy through collaborative processing
Solution Approach 2:
Assistance data acts as an intermediary between network-based calibration information and UE-based positioning calculations. The assistance data contains pre-processed NLOS bias corrections and spatial information that enable UEs to perform accurate positioning calculations locally without requiring continuous network communication, thereby reducing latency
3Measurement precision
If NLOS bias distribution information is collected and processed, then positioning accuracy improves, but device complexity increases
Solution Approach 1:
The system applies partial action by implementing selective NLOS mitigation: full calibration and correction procedures are performed only when NLOS conditions are detected, while LOS conditions use standard positioning methods. This approach achieves accuracy improvements when needed without permanently increasing device complexity for all operating conditions
Data Source
AI summary
Systems, methods, apparatuses, and computer program products for user equipment (UE)-based positioning non-line of sight (NLOS) error mitigation. For example, some embodiments described herein may provide for use of a blind-learning-type algorithm for channel bias distribution estimation for UE-based positioning. The UE may perform a calculation of a positioning of the UE using NLOS bias distribution received from a network node, as described elsewhere herein.