Multi-Point, Multi-Bandwidth RFFP Models for 5G Positioning
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Solution Overview
Problem
Existing wireless communication systems face challenges in achieving highly accurate positioning due to limitations in radio frequency fingerprinting techniques, particularly in 5G networks, which require improved methods for estimating user equipment location using transmission point and bandwidth configurations.
Innovation Solution
A method involving obtaining radio frequency fingerprint positioning (RFFP) measurements associated with known transmission point and bandwidth configurations, and training a positioning model to estimate the location of user equipment based on these measurements and parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional wireless positioning methods are used, then the positioning system is simple to implement, but the positioning accuracy is insufficient for 5G networks
Solution Approach 1:
The patent applies preliminary action by pre-collecting RFFP measurements from multiple transmission points and bandwidth configurations during a training phase, then using these pre-processed measurements to train a positioning model. This allows the system to achieve high positioning accuracy in 5G networks without increasing real-time complexity, as the heavy processing is done in advance during model training rather than during actual positioning operations.
2Measurement precision
If multiple transmission point configurations are used for RFFP measurements, then the positioning accuracy is improved, but the measurement and processing complexity increases
Solution Approach 1:
The patent merges multiple RFFP measurements from different transmission points and bandwidth configurations into a unified positioning model. By combining these diverse measurements during the training phase, the system achieves improved positioning accuracy while managing complexity through centralized model processing rather than separate handling of each measurement source.
Solution Approach 2:
The patent applies parameter changes by varying transmission point configurations and bandwidth settings during the collection of training measurements. This allows the positioning model to learn from diverse parameter combinations, improving its accuracy across different network conditions while the model itself manages the complexity of handling these variable parameters.
3Measurement precision
If RFFP measurements from multiple bandwidth configurations are collected, then the positioning estimate accuracy is enhanced, but the data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing RFFP measurements from multiple bandwidth configurations during the model training phase. This allows the system to handle large volumes of measurement data in advance, reducing the processing burden during real-time positioning operations while maintaining enhanced accuracy from the diverse bandwidth data.
Data Source
AI summary
Methods performed by a network node includes obtaining a plurality of radio frequency fingerprint positioning (RFFP) measurements associated with known positioning parameters associated with a known location of a user equipment (UE) are described. Each RFFP measurement of the plurality of RFFP measurements is further associated with a transmission point configuration of a plurality of transmission point configurations; and training a positioning model to provide an estimate of one or more positioning parameters associated with a location of the UE. The training of the positioning model is based on the plurality of RFFP measurements, known positioning parameters associated with the known location, and the plurality of transmission point configurations.