Positioning Measurement Distributions for Obstructed 5G Location Estimation
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Solution Overview
Problem
Existing positioning methods in wireless communication systems, particularly in 5G networks, face challenges in accurately determining the location of mobile devices due to obstructions that cause overestimation of distances, leading to inaccurate positioning estimates.
Innovation Solution
Mobile devices report a probability distribution of positioning metrics, such as time of arrival and angle of arrival, using parametric representations to improve accuracy by incorporating confidence intervals and variance, allowing networks to combine these distributions for enhanced location estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional positioning methods are used to determine mobile device location, then the positioning process is simple, but the positioning accuracy deteriorates due to obstructions causing non-line-of-sight paths
Solution Approach 1:
The patent transforms positioning measurements from single-point estimates to probability distributions characterized by multiple parameters (mean, variance, skewness, kurtosis). This parametric representation allows the system to capture the uncertainty and multi-path effects in wireless signals, improving positioning accuracy by accounting for the statistical nature of signal propagation through obstructions
Solution Approach 2:
The patent introduces probability distributions as an intermediary layer between raw positioning measurements and final location estimates. This intermediary representation models the uncertainty introduced by non-line-of-sight paths, allowing the system to separate the measurement process from the interpretation process and improve overall positioning reliability
2Measurement precision
If probability distributions are reported for positioning measurements, then positioning accuracy improves, but the amount of data to be transmitted increases
Solution Approach 1:
Instead of transmitting complete probability distribution functions or raw measurement samples, the patent extracts and transmits only the essential parametric characteristics (mean, variance, skewness, kurtosis). This parametric compression maintains the statistical information needed for accurate positioning while dramatically reducing the data transmission volume compared to alternative approaches
Solution Approach 2:
The patent extracts only the most critical statistical parameters from the complete probability distribution that are necessary for positioning accuracy. By selecting and transmitting only these essential parameters rather than the entire distribution, the system achieves a balance between maintaining positioning precision and minimizing data transmission requirements
3Reliability
If multiple positioning measurements are processed to determine probability distributions, then the reliability of position estimates improves, but the processing time increases
Solution Approach 1:
The patent replaces complex iterative statistical analysis methods with direct parametric calculations. Instead of using time-consuming methods like maximum likelihood estimation or Bayesian inference to derive probability distributions, the system directly computes distribution parameters from measurements, significantly reducing processing time while maintaining reliability
Solution Approach 2:
The patent processes multiple positioning measurements to build robust probability distributions, using more measurements than the absolute minimum required. This excessive action approach enhances reliability by充分利用 available measurement data, while the efficient parametric computation method ensures that the additional processing time remains acceptable
Data Source
AI summary
Techniques are provided for determining a position of a mobile device. An example method of reporting a probability distribution for positioning a mobile device includes obtaining positioning measurements, determining one or more probability distributions of one or more positioning metrics based on the positioning measurements, determining a parametric representation of the one or more probability distributions, and reporting the parametric representation.