ML Timing Delay Estimation for Accurate Wireless Positioning
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Solution Overview
Problem
Timing errors in wireless communication systems, particularly in 5G networks, affect positioning accuracy due to uncertainties in transmission and reception time delays, which are not accurately compensated by existing methods.
Innovation Solution
A machine-learning-based approach using radio frequency fingerprints and device locations to estimate signal time delays, employing ML algorithms to convert between wireless and baseband signals, thereby improving timing error compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning algorithms are used to estimate signal time delays, then positioning accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by collecting signal measurements and generating radio frequency fingerprints in advance before actual positioning is needed. The machine learning model is trained beforehand with these pre-collected data, so that during actual positioning operations, the model can quickly estimate time delays without requiring complex real-time computations, thus improving positioning accuracy while managing device complexity.
2Measurement precision
If radio frequency fingerprints and machine learning are employed to compensate for timing errors, then timing error compensation accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary actions by collecting signal measurements and generating radio frequency fingerprints in advance before actual positioning is needed. The machine learning model is trained beforehand with these pre-collected data, so that during actual positioning operations, the model can quickly estimate time delays without requiring complex real-time computations, thus improving positioning accuracy while managing device complexity.
3Measurement precision
If multiple signal measurements and radio frequency fingerprints are collected, then estimation accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by collecting signal measurements and generating radio frequency fingerprints in advance before actual positioning is needed. The machine learning model is trained beforehand with these pre-collected data, so that during actual positioning operations, the model can quickly estimate time delays without requiring complex real-time computations, thus improving positioning accuracy while managing device complexity.
Data Source
AI summary
A signal time delay estimation method includes: obtaining, at an apparatus, a plurality of RFFPs (radio frequency fingerprints) each based on a plurality of signal measurements of respective signals transferred between respective ones of a plurality of wireless signal transfer devices; obtaining, at the apparatus, a plurality of locations corresponding to the plurality of wireless signal transfer devices; and implementing, at the apparatus, a machine-learning algorithm to provide at least one first indication of at least one first signal time delay to convert between a first wireless signal at a target device, of the plurality of wireless signal transfer devices, and a first baseband signal at the target device based on the plurality of RFFPs and the plurality of locations.